# Best Machine Learning Software

*By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)*


Machine learning software leverages algorithms that learn and adapt from data to automate complex decision-making and generate predictions, improving speed and accuracy of outputs over time as the application ingests more training data, with applications spanning process automation, customer service, security risk identification, and contextual collaboration.

### Core Capabilities of Machine Learning Software

To qualify for inclusion in the Machine Learning category, a product must:

- Offer an algorithm that learns and adapts based on data
- Consume data inputs from a variety of data pools
- Ingest data from structured, unstructured, or streaming sources including local files, cloud storage, databases, or APIs
- Be the source of intelligent learning capabilities for applications
- Provide an output that solves a specific issue based on the learned data

### Common Use Cases for Machine Learning Software

Machine learning platforms are used across industries to power intelligent automation and predictive capabilities. Common use cases include:

- Automating complex decisions in financial services, healthcare, and agriculture
- Powering the backend AI that end users interact with in customer-facing applications
- Building and training models for security risk identification and fraud detection

### How Machine Learning Software Differs from Other Tools

End users of machine learning-powered applications do not interact with the algorithm directly, machine learning powers the backend AI layer that users engage with. Machine learning platforms differ from [machine learning operationalization (MLOps) platforms](https://www.g2.com/categories/mlops-platforms) by focusing on model development and training rather than deployment monitoring and lifecycle management.

### Insights from G2 on Machine Learning Software

Based on category trends on G2, flexible data ingestion and model accuracy improvements over time stand out as the most valued capabilities. Ease of integration with existing data infrastructure and the breadth of supported algorithms stand out as key decision factors.






## G2 Grid® for Machine Learning Software
![G2 Grid® for Machine Learning Software plotting products by satisfaction and market presence](https://www.g2.com/categories/machine-learning/grids.png?focus%5B%5D=21469&focus%5B%5D=1327283&focus%5B%5D=1311098&focus%5B%5D=1308795&focus%5B%5D=87432&focus%5B%5D=67046&focus%5B%5D=989&focus%5B%5D=7150)
Highlighted products: Gemini Enterprise Agent Platform, SAS Viya, Azure OpenAI Service, IBM watsonx.ai, Amazon Personalize, Google Cloud TPU, Alteryx, and Dataiku.
Underlying data: [Grid® JSON](https://www.g2.com/categories/machine-learning/grids.json?focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=sas-sas-viya&focus%5B%5D=azure-openai-service&focus%5B%5D=ibm-watsonx-ai&focus%5B%5D=amazon-personalize&focus%5B%5D=google-cloud-tpu&focus%5B%5D=alteryx&focus%5B%5D=dataiku)


## How Many Machine Learning Software Products Does G2 Track?
**Total Products under this Category:** 452

### Category Stats (Jul 2026)
- **Average Rating**: 4.33/5 (↓0.01 vs Jun 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product**: Fireworks AI (+6.67%) - Among all products in this category, Fireworks AI recorded the largest rating increase compared to last month
*Last updated: July 17, 2026*


## How Does G2 Rank Machine Learning Software Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 16,000+ Authentic Reviews
- 452+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.


## Which Machine Learning Software Is Best for Your Use Case?

- **Leader:** [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews)
- **Highest Performer:** [Wiro](https://www.g2.com/products/wiro/reviews)
- **Easiest to Use:** [Azure OpenAI Service](https://www.g2.com/products/azure-openai-service/reviews)
- **Top Trending:** [Google Cloud TPU](https://www.g2.com/products/google-cloud-tpu/reviews)
- **Best Free Software:** [Automation Anywhere Agentic Process Automation](https://www.g2.com/products/automation-anywhere-agentic-process-automation/reviews)


---

**Sponsored**

### SAS Viya

SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control.



[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=684&secure%5Bchosen_at%5D=2026-07-17T20%3A02%3A36Z&secure%5Bdisplayable_resource_id%5D=684&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=684&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1327283&secure%5Bresource_id%5D=684&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fmachine-learning&secure%5Btoken%5D=390d60ddd832e52714cf444bf941b435e70117d4d751dfff36b30891dc282eba&secure%5Burl%5D=https%3A%2F%2Fwww.sas.com%2Fgms%2Fredirect.jsp%3Fdetail%3DPLN73455_275629423&secure%5Burl_type%5D=custom_url)

---

## What Are the Top-Rated Machine Learning Software Products in 2026?
### 1. [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews)
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection.


**Average Rating:** 4.3/5.0
**Total Reviews:** 653
**How Do G2 Users Rate Gemini Enterprise Agent Platform?**

- **Has the product been a good partner in doing business?:** 8.2/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.2/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.1/10 (Category avg: 8.4/10)
- **Ease of Admin:** 7.9/10 (Category avg: 8.5/10)

**Who Is the Company Behind Gemini Enterprise Agent Platform?**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,899,995 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Who Uses This Product?**
- **Who Uses This:** Software Engineer, Data Scientist
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 42% Small-Business, 31% Enterprise


#### What Are Gemini Enterprise Agent Platform's Pros and Cons?

**Pros:**

- Ease of Use (108 reviews)
- Features (77 reviews)
- Machine Learning (76 reviews)
- Model Variety (69 reviews)
- Integrated Platform (66 reviews)

**Cons:**

- Expensive (58 reviews)
- Complexity (48 reviews)
- Learning Curve (48 reviews)
- Complexity Issues (43 reviews)
- Difficult Learning (42 reviews)


### What Do G2 Reviewers Say About Gemini Enterprise Agent Platform?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **ease of use** of Gemini Enterprise Agent Platform, enhancing productivity and streamlining workflows effectively.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity and streamlining machine learning workflows effectively.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity through reduced manual work in projects.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity by streamlining various tasks and processes.
- Users value the **integrated platform** of Gemini, enhancing productivity by combining various functionalities in a unified system.

**Cons:**

- Users find the **pricing ambiguous** with unexpected costs, making budget management a challenge on the Gemini platform.
- Users find the platform's **complexity** ,particularly in navigation and advanced features, challenging, especially for beginners.
- The **learning curve is steep** for new users, especially with complex features and pricing transparency issues.
- Users find the **complexity issues** of the Gemini Enterprise Agent Platform lead to high costs and a steep learning curve.
- Users find the **difficult learning** curve of Gemini Enterprise Agent Platform challenging, especially for newcomers to Google Cloud.

#### What Are Recent G2 Reviews of Gemini Enterprise Agent Platform?

**"[Vertex AI Streamlines ML Training and Deployment with a Unified, Feature-Rich Platform](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)"**

**Rating:** 5.0/5.0 stars
*— Danyal A.*

[Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)

---

**"[Seamless Google Suite Integration for Everyday Work](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480)"**

**Rating:** 4.5/5.0 stars
*— Shubham S.*

[Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480)

---


#### What Are G2 Users Discussing About Gemini Enterprise Agent Platform?

- [What is Google Cloud AI Platform used for?](https://www.g2.com/discussions/what-is-google-cloud-ai-platform-used-for) - 3 comments, 4 upvotes
- [What software libraries does cloud ML engine support?](https://www.g2.com/discussions/what-software-libraries-does-cloud-ml-engine-support) - 3 comments, 4 upvotes
- [How do I use Google cloud platform for machine learning?](https://www.g2.com/discussions/how-do-i-use-google-cloud-platform-for-machine-learning)
- [Is Google Cloud AI free?](https://www.g2.com/discussions/is-google-cloud-ai-free)
- [What is Google AI platform?](https://www.g2.com/discussions/what-is-google-ai-platform) - 2 comments, 2 upvotes

### 2. [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)
SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control.


**Average Rating:** 4.3/5.0
**Total Reviews:** 770
**How Do G2 Users Rate SAS Viya?**

- **Has the product been a good partner in doing business?:** 8.2/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.2/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.4/10 (Category avg: 8.4/10)
- **Ease of Admin:** 7.6/10 (Category avg: 8.5/10)

**Who Is the Company Behind SAS Viya?**

- **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Company Website:** https://www.sas.com/
- **Year Founded:** 1976
- **HQ Location:** Cary, NC
- **Twitter:** @SASsoftware (60,863 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,638 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Student, Biostatistician
- **Top Industries:** Pharmaceuticals, Banking
- **Company Size:** 33% Enterprise, 33% Small-Business


#### What Are SAS Viya's Pros and Cons?

**Pros:**

- Ease of Use (234 reviews)
- Features (218 reviews)
- Analytics (196 reviews)
- Data Analysis (166 reviews)
- Intuitive (145 reviews)

**Cons:**

- Learning Difficulty (151 reviews)
- Learning Curve (144 reviews)
- Complexity (143 reviews)
- Difficult Learning (117 reviews)
- Expensive (108 reviews)


### What Do G2 Reviewers Say About SAS Viya?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **ease of use** of SAS Viya, which simplifies data visualization and enhances decision-making efficiency.
- Users value the **sophisticated analytical capabilities** of SAS Viya, enabling easy deployment and real-time decision-making.
- Users appreciate the **advanced analytical methods** offered by SAS Viya, enhancing decision-making and logistical data analysis capabilities.
- Users value the **end-to-end data lifecycle tooling** of SAS Viya, enhancing business insight and strategic decision-making.
- Users love the **intuitive interface** of SAS Viya, making data analysis and model deployment effortless for all skill levels.

**Cons:**

- Users find SAS Viya to have a **learning difficulty** , making it challenging for non-technical individuals to navigate effectively.
- Users find the **learning curve steep** , making it challenging for non-technical users to navigate SAS Viya effectively.
- Users find the **visualization complexity** in SAS Viya challenging, particularly for non-technical users and beginners.
- Users struggle with the **difficult learning curve** of SAS Viya, particularly for new and non-technical users.
- Users find the **expensive pricing** of SAS Viya to be a significant barrier to entry for potential adoption.

#### What Are Recent G2 Reviews of SAS Viya?

**"[Powerful Integration, Effortless Customization](https://www.g2.com/survey_responses/sas-viya-review-13109700)"**

**Rating:** 5.0/5.0 stars
*— 🇮🇳 Jaimit N.*

[Read full review](https://www.g2.com/survey_responses/sas-viya-review-13109700)

---

**"[Effective Data Analysis with SAS Viya](https://www.g2.com/survey_responses/sas-viya-review-11872818)"**

**Rating:** 4.5/5.0 stars
*— Fungai J.*

[Read full review](https://www.g2.com/survey_responses/sas-viya-review-11872818)

---


#### What Are G2 Users Discussing About SAS Viya?

- [What is SAS Visual Data Mining and Machine Learning used for?](https://www.g2.com/discussions/what-is-sas-visual-data-mining-and-machine-learning-used-for) - 2 comments

### 3. [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews)
Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI lifecycle. With watsonx.ai, you can build, train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with ease and build AI applications in a fraction of the time with a fraction of the data.


**Average Rating:** 4.4/5.0
**Total Reviews:** 134
**How Do G2 Users Rate IBM watsonx.ai?**

- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.8/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.4/10)
- **Ease of Admin:** 8.5/10 (Category avg: 8.5/10)

**Who Is the Company Behind IBM watsonx.ai?**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Company Website:** https://www.ibm.com
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity (74,660 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Consultant
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 40% Small-Business, 32% Enterprise


#### What Are IBM watsonx.ai's Pros and Cons?

**Pros:**

- Ease of Use (67 reviews)
- Model Variety (29 reviews)
- AI Integration (28 reviews)
- Features (26 reviews)
- AI Capabilities (23 reviews)

**Cons:**

- Difficult Learning (20 reviews)
- Complexity (19 reviews)
- Learning Curve (19 reviews)
- Expensive (17 reviews)
- Complex Setup (15 reviews)


### What Do G2 Reviewers Say About IBM watsonx.ai?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **ease of use** in IBM watsonx.ai, facilitating quicker AI integration and effective management.
- Users appreciate the **model variety** of IBM watsonx.ai, enabling customized training on existing models for enhanced performance.
- Users appreciate the **seamless integration of enterprise-grade AI** in IBM watsonx.ai, enhancing decision-making and workflow efficiency.
- Users appreciate the **enterprise-grade integrated studio** of IBM watsonx.ai for seamless AI training and reliable insights.
- Users value the **enterprise-grade AI integration** of IBM watsonx.ai, enhancing decision-making and business operations efficiently.

**Cons:**

- Users find the **difficult learning** curve of IBM watsonx.ai daunting, making it less accessible for newcomers and smaller teams.
- Users find the **complex setup** of IBM watsonx.ai challenging, making it less suitable for small teams and beginners.
- Users find the **steep learning curve** of IBM watsonx.ai challenging, making it less accessible for non-technical teams.
- Users find the product **expensive** and challenging for small teams, citing high costs and complex setup requirements.
- Users find the **complex setup** of IBM watsonx.ai challenging, especially for beginners and small teams.

#### What Are Recent G2 Reviews of IBM watsonx.ai?

**"[Enterprise-Ready Prompt Lab for Comparing Models and Building Project-Based AI Solutions](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13088968)"**

**Rating:** 4.5/5.0 stars
*— Aleksander M.*

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13088968)

---

**"[Enterprise-Ready AI with Strong Governance and Flexible Model Support](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12773148)"**

**Rating:** 4.0/5.0 stars
*— Arkajit D.*

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12773148)

---



### 4. [Azure OpenAI Service](https://www.g2.com/products/azure-openai-service/reviews)
Azure OpenAI Service is a cloud-based platform that provides access to OpenAI's advanced artificial intelligence models, including GPT-3.5, Codex, and DALL·E 2. This service enables developers and businesses to integrate powerful AI capabilities into their applications, facilitating tasks such as natural language processing, code generation, and image creation. By leveraging Azure's enterprise-grade infrastructure, users benefit from enhanced security, compliance, and scalability, making it suitable for a wide range of industries and use cases. Key Features and Functionality: - Access to Advanced AI Models: Utilize state-of-the-art models like GPT-3.5 for natural language understanding, Codex for code generation, and DALL·E 2 for image creation. - Enterprise-Grade Security and Compliance: Benefit from Azure's robust security measures, ensuring data privacy and compliance with industry standards. - Scalability and Reliability: Deploy AI solutions at scale with high availability, leveraging Azure's global infrastructure. - Customization and Fine-Tuning: Tailor AI models to specific business needs through fine-tuning capabilities, enhancing performance for particular tasks. - Integrated Responsible AI Tools: Implement AI solutions responsibly with built-in tools designed to detect and mitigate harmful content, ensuring ethical AI usage. Primary Value and Solutions Provided: Azure OpenAI Service empowers organizations to accelerate innovation by integrating cutting-edge AI models into their products and services. It addresses challenges such as automating complex tasks, enhancing customer interactions through natural language understanding, and generating high-quality content efficiently. By providing a secure and scalable environment, the service enables businesses to harness the full potential of AI while maintaining control over their data and compliance requirements.


**Average Rating:** 4.6/5.0
**Total Reviews:** 58
**How Do G2 Users Rate Azure OpenAI Service?**

- **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.5/10)
- **Quality of Support:** 9.0/10 (Category avg: 8.4/10)
- **Ease of Admin:** 8.9/10 (Category avg: 8.5/10)

**Who Is the Company Behind Azure OpenAI Service?**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,091,739 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (231,632 employees on LinkedIn®)
- **Ownership:** MSFT

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 34% Enterprise, 32% Small-Business


#### What Are Azure OpenAI Service's Pros and Cons?

**Pros:**

- Ease of Use (21 reviews)
- Integrations (18 reviews)
- Scalability (10 reviews)
- Reliability (9 reviews)
- AI Technology (8 reviews)

**Cons:**

- Expensive (14 reviews)
- Complex Setup (7 reviews)
- Limited Features (5 reviews)
- Complexity (4 reviews)
- Time Consumption (4 reviews)


### What Do G2 Reviewers Say About Azure OpenAI Service?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **ease of use** of Azure OpenAI Service, enjoying a smooth setup and beginner-friendly documentation.
- Users value the **seamless integration** of Azure OpenAI Service with Azure, ensuring smooth workflows and efficient implementation.
- Users value the **scalability** of Azure OpenAI Service, facilitating seamless integration into existing workflows across the organization.
- Users praise the **reliability** of Azure OpenAI Service, noting its smooth implementation and consistent performance for daily tasks.
- Users appreciate the **powerful AI capabilities** of Azure OpenAI Service, benefiting from enhanced security and seamless integration.

**Cons:**

- Users often find the **costs and pricing complexity** of Azure OpenAI Service to be confusing and frustrating.
- Users find the **complex setup** challenging, especially for those unfamiliar with the Azure ecosystem.
- Users find the **limited features** of Azure OpenAI Service restrictive, hindering effective use and customization options.
- Users struggle with the **complexity of usage limits and cost tracking** , hindering a seamless experience in Azure OpenAI Service.
- Users find **time consumption** frustrating due to manual quota approvals and complex tracking processes affecting efficiency.

#### What Are Recent G2 Reviews of Azure OpenAI Service?

**"[Secure, Compliant Access to OpenAI Models with Seamless Microsoft Integration](https://www.g2.com/survey_responses/azure-openai-service-review-12838352)"**

**Rating:** 5.0/5.0 stars
*— RaviShankar S.*

[Read full review](https://www.g2.com/survey_responses/azure-openai-service-review-12838352)

---

**"[Reliable AI Platform for Enterprise Automation and Productivity](https://www.g2.com/survey_responses/azure-openai-service-review-12846334)"**

**Rating:** 4.5/5.0 stars
*— Mohit K.*

[Read full review](https://www.g2.com/survey_responses/azure-openai-service-review-12846334)

---



### 5. [Amazon Personalize](https://www.g2.com/products/amazon-personalize/reviews)
Amazon Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications.


**Average Rating:** 4.3/5.0
**Total Reviews:** 33
**How Do G2 Users Rate Amazon Personalize?**

- **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.5/10)
- **Quality of Support:** 9.1/10 (Category avg: 8.4/10)
- **Ease of Admin:** 9.1/10 (Category avg: 8.5/10)

**Who Is the Company Behind Amazon Personalize?**

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud (2,232,483 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (147,094 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services
- **Company Size:** 45% Mid-Market, 45% Small-Business


#### What Are Amazon Personalize's Pros and Cons?

**Pros:**

- Personalization (9 reviews)
- Ease of Use (8 reviews)
- Machine Learning (7 reviews)
- AI Technology (6 reviews)
- Problem Solving (6 reviews)

**Cons:**

- Expensive (8 reviews)
- Difficult Learning (4 reviews)
- Complexity (3 reviews)
- Complex Setup (3 reviews)
- Inaccuracy (3 reviews)


### What Do G2 Reviewers Say About Amazon Personalize?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **relevant and adaptive recommendations** from Amazon Personalize, enhancing user engagement effortlessly over time.
- Users appreciate the **ease of use** of Amazon Personalize for implementing machine learning recommendations effortlessly.
- Users appreciate the **ease of implementing machine learning recommendations** with Amazon Personalize, saving time and enhancing engagement.
- Users admire the **real-time personalization** capabilities of Amazon Personalize, simplifying product recommendations for various industries.
- Users value the **ease of implementing real-time and batch personalization** with Amazon Personalize, enhancing user experiences effortlessly.

**Cons:**

- Users find Amazon Personalize to be **expensive** , especially challenging for smaller projects and startups with limited budgets.
- Users find the **difficult learning** curve challenging, especially beginners adapting to AWS and data pipeline complexities.
- Users find Amazon Personalize **complex to set up and use** , particularly regarding datasets, schemas, and pricing clarity.
- Users find the **complex setup** of Amazon Personalize challenging, especially without prior machine learning experience.
- Users express concern over the **inaccuracy** of recommendations due to limited transparency and reliance on clean data.

#### What Are Recent G2 Reviews of Amazon Personalize?

**"[Reliable AI personalization engine for improving recommendations](https://www.g2.com/survey_responses/amazon-personalize-review-12211914)"**

**Rating:** 5.0/5.0 stars
*— Cristian C.*

[Read full review](https://www.g2.com/survey_responses/amazon-personalize-review-12211914)

---

**"[Accurate, Real-Time Recommendations Without ML Expertise](https://www.g2.com/survey_responses/amazon-personalize-review-12869203)"**

**Rating:** 5.0/5.0 stars
*— Alexis V.*

[Read full review](https://www.g2.com/survey_responses/amazon-personalize-review-12869203)

---



### 6. [Google Cloud TPU](https://www.g2.com/products/google-cloud-tpu/reviews)
Cloud TPU empowers businesses everywhere to access this accelerator technology to speed up their machine learning workloads on Google Cloud


**Average Rating:** 4.5/5.0
**Total Reviews:** 33
**How Do G2 Users Rate Google Cloud TPU?**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.2/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.4/10)
- **Ease of Admin:** 9.2/10 (Category avg: 8.5/10)

**Who Is the Company Behind Google Cloud TPU?**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,899,995 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Who Uses This Product?**
- **Company Size:** 42% Small-Business, 36% Mid-Market


#### What Are Google Cloud TPU's Pros and Cons?

**Pros:**

- Ease of Use (6 reviews)
- Scalability (5 reviews)
- AI Technology (4 reviews)
- Integrations (4 reviews)
- Machine Learning (4 reviews)

**Cons:**

- Difficult Learning (5 reviews)
- Expensive (5 reviews)
- Complex Setup (4 reviews)
- Limited Diversity (4 reviews)
- Learning Curve (3 reviews)


### What Do G2 Reviewers Say About Google Cloud TPU?
*AI-generated summary from verified user reviews*

**Pros:**

- Users find Google Cloud TPU to be incredibly **easy to work with** , enhancing their project efficiency and workflow.
- Users commend the **massive scalability with TPU Pods** , enhancing performance for extensive AI workloads efficiently.
- Users highlight the **high-speed training and seamless AI integration** of Google Cloud TPU, enhancing their deep learning workflows.
- Users value the **seamless integration with frameworks** like TensorFlow and PyTorch, enhancing usability for machine learning projects.
- Users appreciate the **strong performance for large-scale machine learning** with Google Cloud TPU, enhancing their model training efficiency.

**Cons:**

- Users face a **difficult learning curve** with Google Cloud TPU due to its complexity and limited beginner resources.
- Users feel the **costs can add up quickly** with Google Cloud TPU, especially for smaller teams or projects.
- Users experience a **complex setup** with Google Cloud TPU, facing challenges with flexibility and debugging during development.
- Users find the **limited diversity** in frameworks of Google Cloud TPU restricts flexibility for varied machine learning projects.
- Users face a **steep learning curve** with Google Cloud TPU, complicating adoption for those familiar with GPUs.

#### What Are Recent G2 Reviews of Google Cloud TPU?

**"[Blazing-Fast TensorFlow Training with Seamless Google Cloud Integration](https://www.g2.com/survey_responses/google-cloud-tpu-review-12271918)"**

**Rating:** 4.5/5.0 stars
*— Mahmoud H.*

[Read full review](https://www.g2.com/survey_responses/google-cloud-tpu-review-12271918)

---

**"[Impressive Speed and Performance for Large-Scale Model Training](https://www.g2.com/survey_responses/google-cloud-tpu-review-12869092)"**

**Rating:** 5.0/5.0 stars
*— Alexis V.*

[Read full review](https://www.g2.com/survey_responses/google-cloud-tpu-review-12869092)

---



### 7. [Dataiku](https://www.g2.com/products/dataiku/reviews)
Dataiku is the Platform for AI Success: the AI orchestration layer where enterprises build, deploy, and govern analytics, models, and agents at scale. It sits on top of the data platforms, clouds, and AI services you already use, working across all of them without locking you into any one. Dataiku expands who can build production AI, putting the right tools in the hands of data scientists and domain experts alike, from fraud analysts to demand planners. It orchestrates machine learning, rules, LLMs, and agents as one governed system, built on more than a decade of running production AI. Governance is part of the build rather than something bolted on afterward, so teams ship faster while keeping performance, cost, and risk under control. The result: AI that moves from experimentation to trusted, measurable execution now, not in 18 months.


**Average Rating:** 4.4/5.0
**Total Reviews:** 212
**How Do G2 Users Rate Dataiku?**

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.4/10)
- **Ease of Admin:** 8.0/10 (Category avg: 8.5/10)

**Who Is the Company Behind Dataiku?**

- **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku)
- **Company Website:** https://Dataiku.com
- **Year Founded:** 2013
- **HQ Location:** New York, NY
- **Twitter:** @dataiku (22,917 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dataiku/ (1,619 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Data Scientist, Data Analyst
- **Top Industries:** Financial Services, Pharmaceuticals
- **Company Size:** 60% Enterprise, 22% Mid-Market


#### What Are Dataiku's Pros and Cons?

**Pros:**

- Features (80 reviews)
- Ease of Use (79 reviews)
- Usability (45 reviews)
- Easy Integrations (43 reviews)
- Productivity Improvement (41 reviews)

**Cons:**

- Learning Curve (43 reviews)
- Steep Learning Curve (25 reviews)
- Difficult Learning (23 reviews)
- Slow Performance (23 reviews)
- Expensive (22 reviews)


### What Do G2 Reviewers Say About Dataiku?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate how Dataiku facilitates **easy ML development** , allowing focus on building models without the complexity.
- Users love the **ease of use** in Dataiku, simplifying complex tasks and enhancing their data analysis experience.
- Users appreciate the **ease of usability** in Dataiku, enabling collaboration for both technical and non-technical users.
- Users appreciate the **easy integrations** of Dataiku, facilitating smooth collaboration and deployment across various analytics tools.
- Users benefit from the **productivity improvement** of Dataiku, enabling faster project development and enhanced career growth.

**Cons:**

- Users find the **steep learning curve** of Dataiku challenging, making it tough for beginners to master the platform.
- Users find the **steep learning curve** challenging for beginners, impacting their ability to effectively use Dataiku.
- Users find the **difficult learning** curve challenging, particularly for beginners navigating advanced features.
- Users experience **slow performance** with Dataiku when handling large datasets, affecting efficiency and productivity.
- Users find Dataiku **expensive** , especially for smaller organizations and projects, impacting accessibility and affordability.

#### What Are Recent G2 Reviews of Dataiku?

**"[Intuitive and Powerful for Machine Learning Experiments](https://www.g2.com/survey_responses/dataiku-review-13117166)"**

**Rating:** 4.5/5.0 stars
*— jimena m.*

[Read full review](https://www.g2.com/survey_responses/dataiku-review-13117166)

---

**"[Build Faster Workflows with Connected Data from many providers or distinct data sources](https://www.g2.com/survey_responses/dataiku-review-13120436)"**

**Rating:** 4.5/5.0 stars
*— Adalberto G.*

[Read full review](https://www.g2.com/survey_responses/dataiku-review-13120436)

---


#### What Are G2 Users Discussing About Dataiku?

- [Is Dataiku an ETL tool?](https://www.g2.com/discussions/is-dataiku-an-etl-tool)
- [Is Dataiku web based?](https://www.g2.com/discussions/is-dataiku-web-based)
- [What is DSS Dataiku?](https://www.g2.com/discussions/what-is-dss-dataiku)
- [What is Dataiku DSS used for?](https://www.g2.com/discussions/what-is-dataiku-dss-used-for)

### 8. [Alteryx](https://www.g2.com/products/alteryx/reviews)
Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial.


**Average Rating:** 4.6/5.0
**Total Reviews:** 845
**How Do G2 Users Rate Alteryx?**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.4/10)
- **Ease of Admin:** 8.3/10 (Category avg: 8.5/10)

**Who Is the Company Behind Alteryx?**

- **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx)
- **Company Website:** https://www.alteryx.com
- **Year Founded:** 1997
- **HQ Location:** Irvine, CA
- **Twitter:** @alteryx (26,149 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,304 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Data Analyst, Analyst
- **Top Industries:** Financial Services, Accounting
- **Company Size:** 63% Enterprise, 21% Mid-Market


#### What Are Alteryx's Pros and Cons?

**Pros:**

- Ease of Use (333 reviews)
- Automation (148 reviews)
- Intuitive (132 reviews)
- Easy Learning (102 reviews)
- Efficiency (102 reviews)

**Cons:**

- Expensive (88 reviews)
- Learning Curve (80 reviews)
- Missing Features (62 reviews)
- Learning Difficulty (55 reviews)
- Slow Performance (41 reviews)


### What Do G2 Reviewers Say About Alteryx?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **ease of use** of Alteryx, finding it user-friendly and efficient for non-technical users.
- Users appreciate the **automation capabilities** of Alteryx, enhancing speed and efficiency in data preparation and analysis.
- Users love the **intuitive design** of Alteryx, making data management and workflow creation effortless and efficient.
- Users find Alteryx to be **very easy to learn and use** , enhancing their data workflow and automation experience.
- Users appreciate the **efficiency** of Alteryx, enabling quick data processing and streamlined workflows without complex coding.

**Cons:**

- Users mention that Alteryx has a **high cost** which can be challenging for small teams and startups.
- Users find a **steep learning curve** for advanced features, making it challenging for beginners to master Alteryx quickly.
- Users point out the **missing features** in Alteryx, such as limited connectors and issues with output flexibility.
- Users find **learning difficulty** in Alteryx due to confusing tools and troubleshooting errors, especially for beginners.
- Users encounter **slow performance** when processing large datasets, impacting efficiency and usability in Alteryx.

#### What Are Recent G2 Reviews of Alteryx?

**"[Alteryx Streamlines Data Prep with an Intuitive Drag-and-Drop Workflow Builder](https://www.g2.com/survey_responses/alteryx-review-13000974)"**

**Rating:** 4.5/5.0 stars
*— Ankit  K.*

[Read full review](https://www.g2.com/survey_responses/alteryx-review-13000974)

---

**"[Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights](https://www.g2.com/survey_responses/alteryx-review-12983224)"**

**Rating:** 4.5/5.0 stars
*— Akhil S.*

[Read full review](https://www.g2.com/survey_responses/alteryx-review-12983224)

---



### 9. [Amazon Forecast](https://www.g2.com/products/amazon-forecast/reviews)
Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts.


**Average Rating:** 4.3/5.0
**Total Reviews:** 102
**How Do G2 Users Rate Amazon Forecast?**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.4/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.4/10)
- **Ease of Admin:** 7.9/10 (Category avg: 8.5/10)

**Who Is the Company Behind Amazon Forecast?**

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud (2,232,483 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (147,094 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Who Uses This Product?**
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 49% Small-Business, 37% Mid-Market


#### What Are Amazon Forecast's Pros and Cons?

**Pros:**

- Ease of Use (14 reviews)
- Forecasting Accuracy (13 reviews)
- Accuracy (11 reviews)
- Machine Learning (10 reviews)
- Quality (7 reviews)

**Cons:**

- Expensive (11 reviews)
- Complexity (9 reviews)
- Learning Curve (6 reviews)
- Cost Issues (5 reviews)
- Large Dataset Handling (5 reviews)


### What Do G2 Reviewers Say About Amazon Forecast?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **ease of use** of Amazon Forecast, enabling quick and accurate predictions without extensive expertise.
- Users appreciate the **forecasting accuracy** of Amazon Forecast, enabling effective planning and decision-making with ease.
- Users appreciate the **accuracy of time-series predictions** in Amazon Forecast, simplifying demand planning and decision-making.
- Users appreciate the **easy integration and accurate predictions** of Amazon Forecast, simplifying demand planning with minimal ML expertise.
- Users value the **high accuracy** of Amazon Forecast, which utilizes advanced ML technology for reliable results.

**Cons:**

- Users find Amazon Forecast **expensive** for large datasets and frequent predictions, raising concerns about affordability.
- Users find the **complex setup process** of Amazon Forecast challenging, especially for those new to AWS.
- Users experience a challenging **learning curve** due to complex configurations and understanding data schemas before optimal use.
- Users highlight **cost issues** with Amazon Forecast, emphasizing complex pricing and potential for budget surprises with large datasets.
- Users find that managing **large datasets can lead to escalating costs** and a steep learning curve with Amazon Forecast.

#### What Are Recent G2 Reviews of Amazon Forecast?

**"[Accurate ML Forecasting Without the Expertise—Seamless AWS Integration](https://www.g2.com/survey_responses/amazon-forecast-review-12255988)"**

**Rating:** 4.5/5.0 stars
*— Mahmoud H.*

[Read full review](https://www.g2.com/survey_responses/amazon-forecast-review-12255988)

---

**"[Amazon Forecast: Game-Changing Sales Predictor for Training Pros](https://www.g2.com/survey_responses/amazon-forecast-review-12216415)"**

**Rating:** 4.5/5.0 stars
*— Dhanesh V.*

[Read full review](https://www.g2.com/survey_responses/amazon-forecast-review-12216415)

---



### 10. [Apple](https://www.g2.com/products/apple/reviews)
Apple's machine learning (ML) initiatives are designed to seamlessly integrate advanced ML capabilities into its products and services, enhancing user experiences across various devices. By leveraging on-device processing, Apple ensures that ML tasks are performed efficiently and securely, prioritizing user privacy. The company's ML technologies power features such as intelligent photo and video analysis, natural language processing for Siri, and personalized recommendations in apps like Apple Music and News. Key Features and Functionality: - On-Device Processing: Executes ML tasks directly on devices, ensuring faster performance and enhanced privacy by minimizing data transmission. - Core ML Framework: Provides developers with tools to integrate ML models into their apps, supporting a wide range of model types and formats. - Neural Engine: A dedicated hardware component in Apple devices optimized for ML tasks, delivering high-performance processing for complex computations. - Natural Language Processing: Powers features like Siri and text prediction by understanding and generating human language. - Computer Vision: Enables advanced image and video analysis, facilitating functionalities like facial recognition and scene detection. Primary Value and User Solutions: Apple's ML technologies enhance device functionality by providing intelligent, personalized experiences while maintaining user privacy. By processing data on-device, Apple minimizes reliance on cloud services, reducing latency and potential security risks. This approach empowers developers to create innovative applications that leverage ML capabilities, offering users smarter and more responsive interactions with their devices.


**Average Rating:** 4.9/5.0
**Total Reviews:** 19
**How Do G2 Users Rate Apple?**

- **Ease of Use:** 9.6/10 (Category avg: 8.5/10)
- **Quality of Support:** 9.5/10 (Category avg: 8.4/10)

**Who Is the Company Behind Apple?**

- **Seller:** [OpenELM by Apple](https://www.g2.com/sellers/openelm-by-apple)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 79% Small-Business, 21% Mid-Market


#### What Are Apple's Pros and Cons?

**Pros:**

- Ease of Use (8 reviews)
- Quality (4 reviews)
- Reliability (4 reviews)
- Intuitive (3 reviews)
- Technology Advancements (2 reviews)

**Cons:**

- Expensive (5 reviews)
- Limited Customization (3 reviews)
- Expensive Subscriptions (2 reviews)
- Compatibility Issues (1 reviews)
- Complex Setup (1 reviews)


### What Do G2 Reviewers Say About Apple?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **ease of use** across Apple's products, enhancing efficiency and providing a seamless experience.
- Users value the **exceptional quality** of Apple products, enjoying seamless integration and a premium user experience.
- Users appreciate the **reliability** of Apple products, enjoying seamless integration and efficient support across their ecosystem.
- Users love the **intuitive design** of Apple products, enhancing efficiency and simplifying their daily tasks effortlessly.
- Users feel proud of Apple for its **cutting-edge technology advancements** that enhance safety and device performance.

**Cons:**

- Users are often frustrated by the **high prices** of Apple products, limiting their options and flexibility.
- Users are frustrated by Apple's **limited customization** , which restricts flexibility and makes upgrading expensive.
- Users find Apple's pricing model **expensive** , especially for repairs and limited customization options within the ecosystem.
- Users face **compatibility issues** when using Apple products with non-Apple devices, limiting their overall experience.
- Users express frustration with the **complex setup** of Apple devices, hindering seamless integration and flexibility in usage.

#### What Are Recent G2 Reviews of Apple?

**"[Decades with Apple: #1 GUI and Ease of Use](https://www.g2.com/survey_responses/apple-review-12738821)"**

**Rating:** 5.0/5.0 stars
*— Robert M.*

[Read full review](https://www.g2.com/survey_responses/apple-review-12738821)

---

**"[Why Apple Makes My Everyday Tech Work Better Together](https://www.g2.com/survey_responses/apple-review-12772237)"**

**Rating:** 5.0/5.0 stars
*— Michelle M.*

[Read full review](https://www.g2.com/survey_responses/apple-review-12772237)

---



### 11. [machine-learning in Python](https://www.g2.com/products/machine-learning-in-python/reviews)
The "machine-learning" project by jeff1evesque is a Python-based web interface and REST API designed for performing classification and regression tasks. It provides a user-friendly platform for implementing machine learning models, making it accessible for both beginners and experienced practitioners. Key Features and Functionality: - Web Interface: Offers an intuitive graphical user interface for managing datasets, training models, and visualizing results. - REST API: Enables seamless integration with other applications, allowing for automated machine learning workflows. - Classification and Regression: Supports a variety of algorithms to handle both classification and regression problems effectively. - Documentation: Comprehensive guides and resources are available to assist users in understanding and utilizing the platform's capabilities. Primary Value and User Solutions: This project simplifies the process of deploying machine learning models by providing a cohesive environment that combines data management, model training, and result analysis. It addresses common challenges in machine learning implementation, such as the need for coding expertise and integration complexities, thereby enabling users to focus on deriving insights and making data-driven decisions.


**Average Rating:** 4.6/5.0
**Total Reviews:** 49
**How Do G2 Users Rate machine-learning in Python?**

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.4/10)
- **Ease of Admin:** 8.9/10 (Category avg: 8.5/10)

**Who Is the Company Behind machine-learning in Python?**

- **Seller:** [machine-learning in Python](https://www.g2.com/sellers/machine-learning-in-python)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 39% Small-Business, 33% Enterprise


#### What Are machine-learning in Python's Pros and Cons?

**Pros:**

- Machine Learning (10 reviews)
- Ease of Use (8 reviews)
- Model Variety (4 reviews)
- Intuitive (3 reviews)
- Quality (3 reviews)

**Cons:**

- Difficult Learning (3 reviews)
- Dependency Issues (2 reviews)
- Slow Performance (2 reviews)
- Slow Speed (2 reviews)
- AI Limitations (1 reviews)


### What Do G2 Reviewers Say About machine-learning in Python?
*AI-generated summary from verified user reviews*

**Pros:**

- Users love the **rich ecosystem of libraries** in Python that enhance machine learning model development and experimentation.
- Users appreciate the **ease of use** in Python for machine learning, simplifying development with powerful libraries and tools.
- Users value the **model variety** in Python's machine learning libraries, enabling diverse and effective solutions for various problems.
- Users appreciate the **intuitive nature** of Python for machine learning, making it easy to learn and apply.
- Users love the **robust libraries** in Python machine learning, enhancing model building and simplifying data preparation.

**Cons:**

- Users find **difficult learning** curves due to prerequisites and confusing aspects, making initial usage challenging.
- Users face significant **dependency issues** with version conflicts and library coordination, complicating their machine learning experience.
- Users experience **slow performance** with machine learning in Python, especially when handling large datasets and dependencies.
- Users find the **slow speed** of machine learning in Python frustrating, particularly during resource-intensive model training.
- Users note that **performance limitations** in Python can hinder large-scale, compute-intensive machine learning tasks.

#### What Are Recent G2 Reviews of machine-learning in Python?

**"[Streamlined Model Training with Python, Needs Faster Inference](https://www.g2.com/survey_responses/machine-learning-in-python-review-9141715)"**

**Rating:** 4.0/5.0 stars
*— Dev Saran S.*

[Read full review](https://www.g2.com/survey_responses/machine-learning-in-python-review-9141715)

---

**"[Highly Versatile, Perfect for Data Analysis](https://www.g2.com/survey_responses/machine-learning-in-python-review-12985412)"**

**Rating:** 4.5/5.0 stars
*— CHRIS R.*

[Read full review](https://www.g2.com/survey_responses/machine-learning-in-python-review-12985412)

---


#### What Are G2 Users Discussing About machine-learning in Python?

- [What is machine-learning in Python used for?](https://www.g2.com/discussions/what-is-machine-learning-in-python-used-for)
- [How Python is used in AI?](https://www.g2.com/discussions/how-python-is-used-in-ai)
- [Is Python fast enough for machine learning?](https://www.g2.com/discussions/is-python-fast-enough-for-machine-learning)
- [Which Python version is best for machine learning?](https://www.g2.com/discussions/which-python-version-is-best-for-machine-learning) - 2 comments
- [What is Python with machine learning?](https://www.g2.com/discussions/what-is-python-with-machine-learning) - 1 comment

### 12. [NVIDIA Merlin](https://www.g2.com/products/nvidia-merlin/reviews)
NVIDIA Merlin empowers data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes libraries, methods, and tools that streamline the building of recommenders by addressing common preprocessing, feature engineering, training, inference, and deploying to production challenges. Merlin components and capabilities are optimized to support the retrieval, filtering, scoring, and ordering of hundreds of terabytes of data, all accessible through easy-to-use APIs. With Merlin, better predictions, increased click-through rates, and faster deployment to production are within reach.


**Average Rating:** 4.5/5.0
**Total Reviews:** 12
**How Do G2 Users Rate NVIDIA Merlin?**

- **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.2/10 (Category avg: 8.5/10)
- **Quality of Support:** 9.2/10 (Category avg: 8.4/10)
- **Ease of Admin:** 7.8/10 (Category avg: 8.5/10)

**Who Is the Company Behind NVIDIA Merlin?**

- **Seller:** [NVIDIA](https://www.g2.com/sellers/nvidia)
- **Year Founded:** 1993
- **HQ Location:** Santa Clara, CA
- **Twitter:** @nvidia (2,582,827 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3608/ (48,229 employees on LinkedIn®)
- **Ownership:** NVDA

**Who Uses This Product?**
- **Company Size:** 50% Small-Business, 25% Mid-Market


#### What Are NVIDIA Merlin's Pros and Cons?

**Pros:**

- Scalability (4 reviews)
- Ease of Use (3 reviews)
- Quality (3 reviews)
- Reliability (3 reviews)
- AI Technology (1 reviews)

**Cons:**

- Expensive (2 reviews)
- Complexity (1 reviews)
- Complex Setup (1 reviews)
- Data Security (1 reviews)
- Dependency Issues (1 reviews)


### What Do G2 Reviewers Say About NVIDIA Merlin?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **scalability** of NVIDIA Merlin for efficiently handling large datasets in recommender systems.
- Users appreciate the **ease of use** of NVIDIA Merlin, facilitating efficient building and deployment of recommender systems.
- Users appreciate the **unprecedented acceleration** of recommender systems with NVIDIA Merlin, dramatically enhancing efficiency and performance.
- Users highlight the **reliability** of NVIDIA Merlin, ensuring consistent deployment and efficient performance in recommender systems.
- Users highlight the **accelerated recommender systems** support on NVIDIA GPUs, enhancing performance with efficient dataset processing.

**Cons:**

- Users find the **high cost** of NVIDIA Merlin to be a significant barrier to accessing its benefits.
- Users face **complexity challenges** with NVIDIA Merlin due to its steep learning curve and customization difficulties.
- Users face a **complex setup** with NVIDIA Merlin, struggling with its steep learning curve and integration challenges.
- Users express concerns over **data security** and experience **slow inference** with NVIDIA Merlin on a single tower.
- Users face **GPU dependency issues** , limiting access to NVIDIA Merlin's full performance without the necessary hardware resources.

#### What Are Recent G2 Reviews of NVIDIA Merlin?

**"[Revolutionary Acceleration for Recommender Systems](https://www.g2.com/survey_responses/nvidia-merlin-review-12089378)"**

**Rating:** 4.0/5.0 stars
*— Ankit  P.*

[Read full review](https://www.g2.com/survey_responses/nvidia-merlin-review-12089378)

---

**"[Fast, Scalable Recommendation Systems That Save Development Time](https://www.g2.com/survey_responses/nvidia-merlin-review-12682083)"**

**Rating:** 4.0/5.0 stars
*— Noor A.*

[Read full review](https://www.g2.com/survey_responses/nvidia-merlin-review-12682083)

---



### 13. [GoLearn](https://www.g2.com/products/golearn/reviews)
GoLearn is a 'batteries included' machine learning library for Go that implements the scikit-learn interface of Fit/Predict, to easily swap out estimators for trial and error it includes helper functions for data, like cross validation, and train and test splitting.


**Average Rating:** 4.3/5.0
**Total Reviews:** 21
**How Do G2 Users Rate GoLearn?**

- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.1/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.8/10 (Category avg: 8.4/10)
- **Ease of Admin:** 9.0/10 (Category avg: 8.5/10)

**Who Is the Company Behind GoLearn?**

- **Seller:** [GoLearn](https://www.g2.com/sellers/golearn)
- **Year Founded:** 2017
- **HQ Location:** Ballerup, Hovedstaden
- **LinkedIn® Page:** https://www.linkedin.com/company/golearndk/ (67 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services
- **Company Size:** 48% Mid-Market, 43% Small-Business


#### What Are GoLearn's Pros and Cons?

**Pros:**

- Ease of Use (8 reviews)
- Intuitive (4 reviews)
- Navigation Ease (4 reviews)
- Flexibility (2 reviews)
- Interface Clarity (2 reviews)

**Cons:**

- Limited Customization (3 reviews)
- Limited Features (3 reviews)
- Inadequate Search Functionality (2 reviews)
- Limited Diversity (2 reviews)
- Complex Setup (1 reviews)


### What Do G2 Reviewers Say About GoLearn?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **ease of use** of GoLearn, enjoying a straightforward interface for effective learning and skill development.
- Users appreciate the **intuitive interface** of GoLearn, allowing easy navigation and effective management of learning content.
- Users appreciate the **easy navigation** of GoLearn, enhancing their structured learning experience and keeping them organized.
- Users value the **flexibility** of GoLearn, enabling learning on various topics from any device with ease.
- Users appreciate the **easy-to-use interface** of GoLearn, making course management and navigation straightforward and efficient.

**Cons:**

- Users find the **limited customization** options in GoLearn restrictive, impacting course design and user experience.
- Users find **limited features** in GoLearn, leading to surface-level content and restricted customization options.
- Users struggle with **inadequate search functionality** , often scrolling extensively to find specific topics effectively.
- Users feel the **limited diversity** of courses on GoLearn may not meet advanced learning needs effectively.
- Users find the **complex setup** of GoLearn challenging, wishing for more templates and customization options.

#### What Are Recent G2 Reviews of GoLearn?

**"[GoLearn’s Intuitive, Modern Interface Makes Training Easy to Navigate](https://www.g2.com/survey_responses/golearn-review-12869297)"**

**Rating:** 5.0/5.0 stars
*— Alexis V.*

[Read full review](https://www.g2.com/survey_responses/golearn-review-12869297)

---

**"[An intuitive platform that simplifies learning management and content creation](https://www.g2.com/survey_responses/golearn-review-12242125)"**

**Rating:** 4.0/5.0 stars
*— Rodrigo M.*

[Read full review](https://www.g2.com/survey_responses/golearn-review-12242125)

---


#### What Are G2 Users Discussing About GoLearn?

- [What is GoLearn used for?](https://www.g2.com/discussions/what-is-golearn-used-for)

### 14. [AIToolbox](https://www.g2.com/products/aitoolbox/reviews)
AIToolbox is a comprehensive Swift framework designed to facilitate the development and implementation of artificial intelligence algorithms. It offers a suite of AI modules that cater to various machine learning tasks, making it a valuable resource for developers and researchers working within the Swift ecosystem. Key Features and Functionality: - Graphs and Trees: Provides data structures and algorithms for constructing and manipulating graphs and trees, essential for tasks like decision-making processes and hierarchical data representation. - Support Vector Machines (SVMs): Includes tools for implementing SVMs, enabling classification and regression analysis by finding optimal hyperplanes in high-dimensional spaces. - Neural Networks: Offers components to build and train neural networks, facilitating deep learning applications such as image and speech recognition. - Principal Component Analysis (PCA): Contains modules for dimensionality reduction through PCA, aiding in data visualization and noise reduction. - K-Means Clustering: Provides algorithms for partitioning datasets into clusters, useful in pattern recognition and data mining. - Genetic Algorithms: Includes tools for optimization problems using genetic algorithms, simulating natural selection processes to find optimal solutions. Primary Value and User Solutions: AIToolbox addresses the need for a native Swift library that encompasses a broad range of AI functionalities. By integrating multiple machine learning modules into a single framework, it simplifies the development process for Swift developers, eliminating the need to rely on external libraries or languages. This consolidation enhances efficiency, promotes code consistency, and accelerates the deployment of AI-driven applications on Apple platforms.


**Average Rating:** 4.4/5.0
**Total Reviews:** 35
**How Do G2 Users Rate AIToolbox?**

- **Has the product been a good partner in doing business?:** 8.1/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.8/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.9/10 (Category avg: 8.4/10)
- **Ease of Admin:** 8.6/10 (Category avg: 8.5/10)

**Who Is the Company Behind AIToolbox?**

- **Seller:** [AIToolbox](https://www.g2.com/sellers/aitoolbox)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 54% Small-Business, 40% Mid-Market


#### What Are AIToolbox's Pros and Cons?

**Pros:**

- Ease of Use (10 reviews)
- Model Variety (5 reviews)
- AI Technology (4 reviews)
- Integrations (3 reviews)
- Features (2 reviews)

**Cons:**

- Inaccuracy (3 reviews)
- Limited Features (2 reviews)
- AI Limitations (1 reviews)
- Compatibility Issues (1 reviews)
- Complex Setup (1 reviews)


### What Do G2 Reviewers Say About AIToolbox?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **ease of use** of AIToolbox, finding it convenient to access various AI tools all in one place.
- Users appreciate the **model variety** of AIToolbox, making it easy to find and use essential AI tools.
- Users value the **comprehensive AI coverage** of AIToolbox, enhancing convenience and efficiency in development projects.
- Users appreciate the **easy integrations** with various AI modules, enhancing development speed and convenience in implementation.
- Users appreciate the **wide range of AI tools** in AIToolbox, making experimentation and application easy and efficient.

**Cons:**

- Users struggle with the **inaccuracy** of AIToolbox, facing issues like unexplained flags and hallucinatory logic during analyses.
- Users note the **limited features** of AIToolbox, often finding them basic and lacking advanced customization options.
- Users are frustrated with the **lack of transparency and frequent errors** in AIToolbox's AI functionality during transactions.
- Users report **compatibility issues** with the MINOR version, causing disruptions in production and issues with high-frequency database operations.
- Users find the **complex setup** of AIToolbox challenging, suggesting improvements for easier integration and workflow efficiency.

#### What Are Recent G2 Reviews of AIToolbox?

**"[Generous Free Tier and Easy AI-Powered Social Post Creation](https://www.g2.com/survey_responses/aitoolbox-review-12213473)"**

**Rating:** 5.0/5.0 stars
*— Tony P.*

[Read full review](https://www.g2.com/survey_responses/aitoolbox-review-12213473)

---

**"[Flexible Integration and Powerful Automation in One Platform](https://www.g2.com/survey_responses/aitoolbox-review-12473904)"**

**Rating:** 4.5/5.0 stars
*— Alexis V.*

[Read full review](https://www.g2.com/survey_responses/aitoolbox-review-12473904)

---


#### What Are G2 Users Discussing About AIToolbox?

- [What is AIToolbox used for?](https://www.g2.com/discussions/what-is-aitoolbox-used-for) - 1 comment

### 15. [Personalizer](https://www.g2.com/products/personalizer/reviews)
Recommendations API is a tool that helps customer discover items in users catalog, customer activity in a user's digital store is used to recommend items and to improve conversion in digital store.


**Average Rating:** 4.2/5.0
**Total Reviews:** 28
**How Do G2 Users Rate Personalizer?**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.4/10)
- **Ease of Admin:** 8.0/10 (Category avg: 8.5/10)

**Who Is the Company Behind Personalizer?**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,091,739 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (231,632 employees on LinkedIn®)
- **Ownership:** MSFT

**Who Uses This Product?**
- **Company Size:** 50% Small-Business, 32% Enterprise


#### What Are Personalizer's Pros and Cons?

**Pros:**

- Personalization (3 reviews)
- Problem Solving (2 reviews)
- AI Technology (1 reviews)
- Ease of Use (1 reviews)
- Integrations (1 reviews)

**Cons:**

- Complex Setup (2 reviews)
- Difficult Learning (2 reviews)
- AI Limitations (1 reviews)
- Difficulty for Beginners (1 reviews)
- Time Consumption (1 reviews)


### What Do G2 Reviewers Say About Personalizer?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **real-time AI-driven personalization** of Personalizer, enhancing experiences through continuous learning and adaptability.
- Users value the **dynamic problem-solving capabilities** of Personalizer, enhancing content relevancy through real-time learning from user behavior.
- Users appreciate the **real-time, AI-driven personalization** of Personalizer, simplifying the process of delivering tailored experiences.
- Users find the **ease of use** of Personalizer remarkable, as it quickly adapts to enhance content engagement.
- Users appreciate the **seamless integration** of Personalizer with Azure, enhancing deployment and management of personalized experiences.

**Cons:**

- Users find the **complex setup** of Personalizer challenging, requiring extensive initial configuration and ongoing monitoring.
- Users find the **difficult learning curve** of Personalizer frustrating, requiring significant time and effort for initial setup.
- Users find that Personalizer provides **robotic responses** too often, reducing the personal touch in interactions.
- The setup of Personalizer is **not beginner-friendly** , requiring trial and error to understand rewards and signals.
- Users find the **time consumption for initial configuration** and monitoring a challenge during early use of Personalizer.

#### What Are Recent G2 Reviews of Personalizer?

**"[Real-Time Personalization That Boosts Conversions and ROI](https://www.g2.com/survey_responses/personalizer-review-13059466)"**

**Rating:** 4.0/5.0 stars
*— Michele C.*

[Read full review](https://www.g2.com/survey_responses/personalizer-review-13059466)

---

**"[Intuitive, Flexible Personalization with Powerful Automation and Real-Time Analytics](https://www.g2.com/survey_responses/personalizer-review-12811865)"**

**Rating:** 4.5/5.0 stars
*— Brandon R.*

[Read full review](https://www.g2.com/survey_responses/personalizer-review-12811865)

---



### 16. [Wiro](https://www.g2.com/products/wiro/reviews)
Wiro is a unified AI API and generative AI infrastructure platform designed to help organizations build, deploy, and scale AI-powered applications through a single integration. The platform enables developers to access large language models (LLMs), AI image generation models, text-to-video and image-to-video models, speech-to-text systems, and real-time conversational AI through one standardized API. Wiro is particularly suited for teams building AI video generator apps, AI image generation tools, AI chatbots, voice assistant platforms, and other generative AI SaaS products. Instead of integrating multiple providers separately, developers can use Wiro as a centralized AI integration layer that abstracts GPU infrastructure, model hosting, and vendor management. Beyond simple API aggregation, Wiro supports model operationalization, including fine-tuning workflows (such as LoRA and DreamBooth), reusable AI pipelines, and RAG (retrieval-augmented generation) architectures. Teams can train custom models, deploy fine-tuned versions, and orchestrate multi-model workflows within the same application pipeline. This makes Wiro suitable for production AI deployment, multi-model orchestration, and scalable AI integration in real-world applications. The platform hosts and optimizes open-source foundation models on dedicated GPU infrastructure while also providing unified access to commercial AI providers such as OpenAI and Google. Its centralized architecture supports intelligent routing, workload scheduling, monitoring, and high-throughput API traffic management. Wiro operates on a transparent, usage-based pricing model where customers are billed per API request based on compute and token usage. This approach allows startups, SaaS companies, and enterprise teams to scale AI workloads without long-term infrastructure commitments. By combining unified AI APIs, model fine-tuning, workflow orchestration, and multi-provider integration, Wiro functions as an AI infrastructure layer and OpenAI alternative API for teams building AI video apps, AI image generation platforms, conversational AI systems, and production-ready generative AI solutions.


**Average Rating:** 4.9/5.0
**Total Reviews:** 28
**How Do G2 Users Rate Wiro?**

- **Ease of Use:** 9.7/10 (Category avg: 8.5/10)
- **Quality of Support:** 9.8/10 (Category avg: 8.4/10)

**Who Is the Company Behind Wiro?**

- **Seller:** [Wiro.ai](https://www.g2.com/sellers/wiro-ai)
- **Company Website:** https://www.wiro.ai
- **Year Founded:** 2023
- **HQ Location:** San Francisco, CA
- **Twitter:** @wiroai (1,534 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/wiroai (24 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Marketing and Advertising
- **Company Size:** 97% Small-Business, 3% Mid-Market



#### What Are Recent G2 Reviews of Wiro?

**"[The Easiest Way to Centralize AI Media Models](https://www.g2.com/survey_responses/wiro-review-12557513)"**

**Rating:** 5.0/5.0 stars
*— Altan K.*

[Read full review](https://www.g2.com/survey_responses/wiro-review-12557513)

---

**"[Flexible APIs That Let You Build Anything](https://www.g2.com/survey_responses/wiro-review-12703223)"**

**Rating:** 5.0/5.0 stars
*— Metehan K.*

[Read full review](https://www.g2.com/survey_responses/wiro-review-12703223)

---



### 17. [UiPath Document Understanding](https://www.g2.com/products/uipath-uipath-document-understanding/reviews)
UiPath Document Understanding is an AI-powered solution that automates the extraction, classification, and validation of data from a wide range of document types, including structured, semi-structured, and unstructured formats. By integrating advanced machine learning models with robotic process automation (RPA), it enables organizations to process documents such as invoices, contracts, and compliance reports with increased speed and accuracy, reducing manual effort and minimizing errors. Key Features and Functionality: - AI-Powered Data Extraction: Utilizes machine learning models to accurately extract information from various document formats, including text, handwriting, tables, and checkboxes. - Pre-Trained and Custom Models: Offers pre-built models for common document types and the flexibility to train custom models tailored to specific business needs. - Human-in-the-Loop Validation: Incorporates human validation stations to review and correct extracted data, ensuring high accuracy and compliance. - Seamless Integration: Integrates with UiPath Studio and Orchestrator, allowing for the creation of end-to-end automation workflows that connect document processing with broader business processes. - Flexible Deployment Options: Supports both on-premises and cloud deployments to align with various IT infrastructures and compliance requirements. Primary Value and Problem Solved: UiPath Document Understanding addresses the challenges associated with manual document processing, such as time consumption, susceptibility to errors, and scalability limitations. By automating the extraction and processing of data from diverse document types, it enhances operational efficiency, reduces processing times, and improves data accuracy. This leads to better compliance, cost savings, and the ability to scale operations effectively, ultimately delivering a superior customer experience.


**Average Rating:** 4.6/5.0
**Total Reviews:** 21
**How Do G2 Users Rate UiPath Document Understanding?**

- **Has the product been a good partner in doing business?:** 6.7/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.5/10)
- **Quality of Support:** 9.1/10 (Category avg: 8.4/10)
- **Ease of Admin:** 7.8/10 (Category avg: 8.5/10)

**Who Is the Company Behind UiPath Document Understanding?**

- **Seller:** [UiPath](https://www.g2.com/sellers/uipath)
- **Year Founded:** 2005
- **HQ Location:** New York
- **Twitter:** @UiPath (108,104 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1523656/ (5,134 employees on LinkedIn®)
- **Ownership:** NYSE: PATH

**Who Uses This Product?**
- **Company Size:** 55% Small-Business, 27% Enterprise


#### What Are UiPath Document Understanding's Pros and Cons?

**Pros:**

- Machine Learning (2 reviews)
- AI Technology (1 reviews)
- Analytics (1 reviews)
- Annotation Tools (1 reviews)
- Automation (1 reviews)

**Cons:**

- Automation Challenges (1 reviews)
- Dependency Issues (1 reviews)
- Document Management (1 reviews)
- Inaccuracy (1 reviews)
- Learning Difficulty (1 reviews)


### What Do G2 Reviewers Say About UiPath Document Understanding?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **machine learning capabilities** of UiPath Document Understanding for simplifying data extraction from unstructured documents.
- Users value the **active learning support** in UiPath Document Understanding, easing model training for non-ML experts.
- Users appreciate the **analytics capabilities** of UiPath Document Understanding for efficiently processing and extracting data from unstructured documents.
- Users appreciate the **AI-guided annotation tools** of UiPath Document Understanding, simplifying model training for non-ML experts.
- Users value the **automation of unstructured document processing** , enhancing customization and efficiency in data extraction.

**Cons:**

- Users face **automation challenges** due to reliance on human intervention for low-confidence fields, impacting efficiency in critical documents.
- Users experience **dependency issues** as reliance on human input hinders automation efficiency, particularly with important documents.
- Users experience **limitations in document management** , preferring flexible area definition over fixed selectable fields for data extraction.
- Users find the **inaccuracy in data extraction** challenging, especially when using smaller datasets for training.
- Users find **learning difficulty** with UiPath Document Understanding, especially when training on smaller datasets impacts accuracy.

#### What Are Recent G2 Reviews of UiPath Document Understanding?

**"[Efficient, Reliable Document Understanding with Seamless RPA Integration](https://www.g2.com/survey_responses/uipath-document-understanding-review-12817862)"**

**Rating:** 4.0/5.0 stars
*— Navin M.*

[Read full review](https://www.g2.com/survey_responses/uipath-document-understanding-review-12817862)

---

**"[Efficient with Structured Data, Needs Improvement for Handwritten Docs](https://www.g2.com/survey_responses/uipath-document-understanding-review-12917631)"**

**Rating:** 4.5/5.0 stars
*— Akshat K.*

[Read full review](https://www.g2.com/survey_responses/uipath-document-understanding-review-12917631)

---



### 18. [Automation Anywhere Agentic Process Automation](https://www.g2.com/products/automation-anywhere-agentic-process-automation/reviews)
Automation Anywhere is the global leader in Agentic Process Automation (APA), transforming how organizations put AI to work. Our platform combines specialized AI agents, generative AI, and end-to-end process automation—including process discovery, RPA, document processing, and advanced analytics—all built on a foundation of security and governance. We empower businesses worldwide to unlock productivity, elevate customer experiences, and drive new revenue streams. Guided by our vision to fuel the future of work, we unleash human potential through Agentic AI-powered automation. Learn more at https://www.automationanywhere.com.


**Average Rating:** 4.5/5.0
**Total Reviews:** 4,053
**How Do G2 Users Rate Automation Anywhere Agentic Process Automation?**

- **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.4/10)
- **Ease of Admin:** 8.4/10 (Category avg: 8.5/10)

**Who Is the Company Behind Automation Anywhere Agentic Process Automation?**

- **Seller:** [Automation Anywhere](https://www.g2.com/sellers/automation-anywhere)
- **Company Website:** https://www.AutomationAnywhere.com
- **Year Founded:** 2003
- **HQ Location:** San Jose, CA
- **Twitter:** @AutomationAnywh (55,050 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/208639/ (4,314 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** RPA Developer, Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 59% Enterprise, 23% Mid-Market


#### What Are Automation Anywhere Agentic Process Automation's Pros and Cons?

**Pros:**

- Ease of Use (84 reviews)
- Automation (71 reviews)
- Efficiency (43 reviews)
- Automation Ease (42 reviews)
- User Interface (42 reviews)

**Cons:**

- Expensive (28 reviews)
- Learning Curve (27 reviews)
- Missing Features (25 reviews)
- Software Bugs (22 reviews)
- Bot Issues (20 reviews)


### What Do G2 Reviewers Say About Automation Anywhere Agentic Process Automation?
*AI-generated summary from verified user reviews*

**Pros:**

- Users find the **ease of use** in Automation Anywhere Agentic Process Automation makes bot development simple and efficient.
- Users love the **ease of automating tasks** with Automation Anywhere, enjoying its intuitive interface and time-saving capabilities.
- Users appreciate the **efficiency** of Automation Anywhere, simplifying automation and enhancing process performance across various applications.
- Users admire the **ease of automation** with Automation Anywhere, enabling non-coders to effortlessly create bots.
- Users appreciate the **intuitive user interface** of Automation Anywhere, enhancing automation ease and efficiency across processes.

**Cons:**

- Users find the **pricing model complex and high** , which can be challenging for small teams or individuals.
- Users face a **steep learning curve** with Automation Anywhere, particularly when navigating advanced features and complex workflows.
- Users note the **missing features** in Automation Anywhere, impacting functionality and complicating automation tasks.
- Users frequently face **software bugs** that disrupt automation tasks and lead to frustrating experiences throughout the process.
- Users struggle with **bot issues** like complex UI, debugging challenges, and the steep learning curve for beginners.

#### What Are Recent G2 Reviews of Automation Anywhere Agentic Process Automation?

**"[Excellent Efficiency Gains, But Onboarding Needs Simplification](https://www.g2.com/survey_responses/automation-anywhere-agentic-process-automation-review-12949275)"**

**Rating:** 4.0/5.0 stars
*— Joel A.*

[Read full review](https://www.g2.com/survey_responses/automation-anywhere-agentic-process-automation-review-12949275)

---

**"[Helping Teams Focus on Higher-Value Work](https://www.g2.com/survey_responses/automation-anywhere-agentic-process-automation-review-12995566)"**

**Rating:** 4.0/5.0 stars
*— pavan k.*

[Read full review](https://www.g2.com/survey_responses/automation-anywhere-agentic-process-automation-review-12995566)

---


#### What Are G2 Users Discussing About Automation Anywhere Agentic Process Automation?

- [What are the most significant impacts of Automation Anywhere's RPA on business process efficiency?](https://www.g2.com/discussions/automation-anywhere-rpa-robotic-process-automation-what-are-the-most-significant-impacts-of-automation-anywhere-s-rpa-on-business-process-efficiency) - 1 comment
- [What are the most significant impacts of Automation Anywhere's RPA on business process efficiency?](https://www.g2.com/discussions/what-are-the-most-significant-impacts-of-automation-anywhere-s-rpa-on-business-process-efficiency) - 3 comments
- [What is Automation Anywhere - RPA | Robotic Process Automation used for?](https://www.g2.com/discussions/what-is-automation-anywhere-rpa-robotic-process-automation-used-for) - 1 comment
- [Can RPA be used for test automation?](https://www.g2.com/discussions/can-rpa-be-used-for-test-automation) - 4 comments, 1 upvote
- [How is RPA different from automation?](https://www.g2.com/discussions/how-is-rpa-different-from-automation)

### 19. [SAP HANA Cloud](https://www.g2.com/products/sap-hana-cloud-2025-10-01/reviews)
SAP HANA Cloud is a modern database-as-a-service (DBaaS) powering the next generation of intelligent data applications. SAP HANA Cloud offers a competitive edge by incorporating advanced machine learning and predictive tools grounded in modern data science. Its powerful in-memory performance safeguards efficient data processing. By securely storing vast amounts of data with its integrated multitier storage and handling various types on a single copy in its native multi-model database, SAP HANA Cloud simplifies data management and connects to other data sources. The seamless integration of these capabilities in a reliable, unified foundation makes it easier for developers to build high-demand intelligent data apps.


**Average Rating:** 4.3/5.0
**Total Reviews:** 521
**How Do G2 Users Rate SAP HANA Cloud?**

- **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.2/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.2/10 (Category avg: 8.4/10)
- **Ease of Admin:** 7.9/10 (Category avg: 8.5/10)

**Who Is the Company Behind SAP HANA Cloud?**

- **Seller:** [SAP](https://www.g2.com/sellers/sap)
- **Company Website:** https://www.sap.com/
- **Year Founded:** 1972
- **HQ Location:** Walldorf
- **Twitter:** @SAP (297,052 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,955 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Consultant, SAP Consultant
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 61% Enterprise, 26% Mid-Market


#### What Are SAP HANA Cloud's Pros and Cons?

**Pros:**

- Ease of Use (55 reviews)
- Easy Integrations (41 reviews)
- Integrations (40 reviews)
- Speed (39 reviews)
- Scalability (35 reviews)

**Cons:**

- Complexity (33 reviews)
- Expensive (32 reviews)
- Learning Curve (30 reviews)
- Difficult Learning (28 reviews)
- Complex Setup (20 reviews)


### What Do G2 Reviewers Say About SAP HANA Cloud?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **ease of use** with SAP HANA Cloud, enjoying seamless integration and smooth real-time analytics.
- Users appreciate the **easy integrations** of SAP HANA Cloud, enhancing their workflow and data management seamlessly.
- Users value the **smooth integration capabilities** of SAP HANA Cloud, enhancing efficiency and simplifying data management.
- Users value the **speed and responsiveness** of SAP HANA Cloud, making data handling efficient and streamlined.
- Users value the **flexible and elastic scalability** of SAP HANA Cloud, enhancing performance for data-intensive applications.

**Cons:**

- Users find the **complexity of the setup** challenging, particularly for new users and integrations with other systems.
- Users find SAP HANA Cloud to be **expensive** , particularly for large data demands and advanced configurations requiring expertise.
- Users find the **learning curve challenging** , especially for teams new to HANA's concepts and SQL extensions.
- Users find the **difficult learning curve** of SAP HANA Cloud challenging, especially for new users and feature navigation.
- Users find the **complex setup** of SAP HANA Cloud challenging, particularly for newcomers and during initial provisioning.

#### What Are Recent G2 Reviews of SAP HANA Cloud?

**"[Blazing-Fast In-Memory Performance with Seamless SAP Integration](https://www.g2.com/survey_responses/sap-hana-cloud-review-12419032)"**

**Rating:** 4.5/5.0 stars
*— Dharamveer p.*

[Read full review](https://www.g2.com/survey_responses/sap-hana-cloud-review-12419032)

---

**"[Efficient Transactions, But Time-Intensive Setup](https://www.g2.com/survey_responses/sap-hana-cloud-review-12983922)"**

**Rating:** 4.0/5.0 stars
*— Sumeet R.*

[Read full review](https://www.g2.com/survey_responses/sap-hana-cloud-review-12983922)

---



### 20. [Pecan](https://www.g2.com/products/pecan/reviews)
Pecan AI is a predictive analytics platform that helps business teams understand what’s likely to happen next, while there is still time to act. With Pecan’s Predictive AI Agent, teams can turn business questions into reliable predictions for use cases like customer churn, demand forecasting, and lifetime value, without relying on long, complex data science projects. The platform automatically handles data preparation, feature engineering, modeling, validation, and delivery, and provides transparent, explainable predictions that integrate into tools like Salesforce, HubSpot, Snowflake, and BI systems to drive real business outcomes.


**Average Rating:** 4.7/5.0
**Total Reviews:** 36
**How Do G2 Users Rate Pecan?**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.5/10)
- **Quality of Support:** 9.8/10 (Category avg: 8.4/10)
- **Ease of Admin:** 7.9/10 (Category avg: 8.5/10)

**Who Is the Company Behind Pecan?**

- **Seller:** [Pecan.ai](https://www.g2.com/sellers/pecan-ai)
- **Company Website:** https://www.pecan.ai
- **Year Founded:** 2018
- **HQ Location:** US, Israel
- **Twitter:** @pecan_ai (1,135 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/pecan-ai/ (89 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Retail
- **Company Size:** 54% Mid-Market, 21% Enterprise


#### What Are Pecan's Pros and Cons?

**Pros:**

- Ease of Use (25 reviews)
- Customer Support (18 reviews)
- Speed (15 reviews)
- Problem Solving (13 reviews)
- Implementation Ease (11 reviews)

**Cons:**

- Learning Difficulty (9 reviews)
- Limitations (8 reviews)
- Limited Features (8 reviews)
- Learning Curve (7 reviews)
- Limited Customization (5 reviews)


### What Do G2 Reviewers Say About Pecan?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **ease of use** of Pecan, allowing simple model building without requiring deep technical skills.
- Users praise Pecan's **excellent customer support** , emphasizing prompt assistance and valuable guidance throughout their learning process.
- Users highlight the **speed of development** with Pecan, reducing model creation from months to weeks efficiently.
- Users value Pecan's **exceptional problem-solving support** , enhancing their ability to leverage data for actionable insights.
- Users highlight the **implementation ease** of Pecan, facilitating swift model deployment and enhancing productivity significantly.

**Cons:**

- Users experience a **steep learning curve** with Pecan, requiring at least intermediate SQL knowledge to navigate effectively.
- Users desire **deeper control over model selection and custom optimization metrics** , finding the auto-selection process limiting.
- Users feel the **limited model customization** restricts their ability to tailor solutions for specific use cases effectively.
- Users experience a **steep learning curve** initially, particularly with data structure and SQL understanding required.
- Users express a desire for **limited customization** , wishing for more control over model selection and optimization metrics.

#### What Are Recent G2 Reviews of Pecan?

**"[AI Chatbox Integration Makes Feature Development Easy to Explore and Iterate](https://www.g2.com/survey_responses/pecan-review-12878894)"**

**Rating:** 4.0/5.0 stars
*— Yuqi L.*

[Read full review](https://www.g2.com/survey_responses/pecan-review-12878894)

---

**"[Intuitive Platform with Exceptional Support](https://www.g2.com/survey_responses/pecan-review-12654479)"**

**Rating:** 5.0/5.0 stars
*— J G.*

[Read full review](https://www.g2.com/survey_responses/pecan-review-12654479)

---



### 21. [scikit-learn](https://www.g2.com/products/scikit-learn/reviews)
Scikit-learn is a software machine learning library for the Python programming language that has a various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.


**Average Rating:** 4.8/5.0
**Total Reviews:** 60
**How Do G2 Users Rate scikit-learn?**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.6/10 (Category avg: 8.5/10)
- **Quality of Support:** 9.4/10 (Category avg: 8.4/10)
- **Ease of Admin:** 9.4/10 (Category avg: 8.5/10)

**Who Is the Company Behind scikit-learn?**

- **Seller:** [scikit-learn](https://www.g2.com/sellers/scikit-learn)
- **Year Founded:** 2018
- **HQ Location:** N/A
- **Twitter:** @scikit_learn (22,807 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/datacollege/

**Who Uses This Product?**
- **Who Uses This:** Senior Software Engineer, Machine Learning Engineer
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 40% Enterprise, 32% Mid-Market


#### What Are scikit-learn's Pros and Cons?

**Pros:**

- Ease of Use (1 reviews)
- Machine Learning (1 reviews)
- Usage Frequency (1 reviews)

**Cons:**

- Lagging Issues (1 reviews)
- Limited Customization (1 reviews)
- Time Consumption (1 reviews)


### What Do G2 Reviewers Say About scikit-learn?
*AI-generated summary from verified user reviews*

**Pros:**

- Users find scikit-learn's **ease of use** beneficial for beginners, simplifying the learning process in machine learning.
- Users appreciate the **clean and intuitive API** of scikit-learn, making it easy for beginners to learn machine learning.
- Users find scikit-learn a **frequently used library** for beginners, thanks to its clean interface and easy access to algorithms.

**Cons:**

- Users experience **lagging issues** with scikit-learn, especially when modeling complex algorithms, affecting overall performance.
- Users face challenges with **limited customization** in scikit-learn, hindering their ability to tweak algorithms effectively.
- Users find that learning scikit-learn takes a significant amount of time due to its **steep learning curve** for beginners.

#### What Are Recent G2 Reviews of scikit-learn?

**"[Python library](https://www.g2.com/survey_responses/scikit-learn-review-8018415)"**

**Rating:** 4.5/5.0 stars
*— Diana B.*

[Read full review](https://www.g2.com/survey_responses/scikit-learn-review-8018415)

---

**"[Perfect Starter Library for Machine Learning Beginners](https://www.g2.com/survey_responses/scikit-learn-review-12106010)"**

**Rating:** 4.5/5.0 stars
*— Verified User in Higher Education*

[Read full review](https://www.g2.com/survey_responses/scikit-learn-review-12106010)

---


#### What Are G2 Users Discussing About scikit-learn?

- [What is scikit-learn used for?](https://www.g2.com/discussions/scikit-learn-what-is-scikit-learn-used-for) - 2 comments
- [Does Scikit learn API?](https://www.g2.com/discussions/does-scikit-learn-api)
- [What is Python Scikit learn?](https://www.g2.com/discussions/what-is-python-scikit-learn) - 1 comment
- [What is Scikit learn used for?](https://www.g2.com/discussions/what-is-scikit-learn-used-for)

### 22. [Minitab Statistical Software](https://www.g2.com/products/minitab-statistical-software/reviews)
Minitab Statistical Software is a comprehensive data analysis and statistical solution designed to assist users in exploring data, identifying trends, and making informed, data-driven decisions. This software caters to a diverse audience, ranging from beginners to seasoned analysts, by combining powerful statistical methods with an intuitive interface. This design approach simplifies complex analyses while ensuring depth and accuracy are not compromised. The capabilities of Minitab are extensive, encompassing a variety of statistical techniques such as descriptive statistics, hypothesis testing, regression analysis, ANOVA, time series analysis, design of experiments (DOE), reliability analysis, and predictive analytics. The software's guided workflows and Assistant feature are particularly beneficial, as they help users select appropriate tools, interpret results effectively, and communicate findings in a clear manner. This accessibility makes advanced analytics feasible for users across different skill levels, promoting a culture of data-driven decision-making within organizations. Minitab is available in both desktop and cloud-based versions, providing users with the flexibility to work from various locations while ensuring secure access to their data and analyses. The cloud version enhances collaboration among teams by allowing them to share projects and standardize analyses without the need for local installation. This feature is particularly advantageous for organizations with remote teams or those looking to streamline their analytical processes. The software also includes built-in data preparation tools that facilitate the cleaning and organization of data, which is crucial for accurate analysis. Furthermore, Minitab seamlessly integrates with other Minitab solutions and supports quality improvement methodologies such as Six Sigma. This integration helps organizations enhance their processes, reduce variation, and achieve measurable business outcomes. By providing a robust suite of tools and features, Minitab Statistical Software stands out as a valuable resource for organizations aiming to leverage data for strategic advantage.


**Average Rating:** 4.6/5.0
**Total Reviews:** 220
**How Do G2 Users Rate Minitab Statistical Software?**

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.8/10 (Category avg: 8.4/10)
- **Ease of Admin:** 8.4/10 (Category avg: 8.5/10)

**Who Is the Company Behind Minitab Statistical Software?**

- **Seller:** [Minitab](https://www.g2.com/sellers/minitab-14ca02fe-fdeb-44c4-b0db-904058d0221b)
- **Company Website:** https://www.minitab.com
- **Year Founded:** 1972
- **HQ Location:** State College, Pennsylvania, United States
- **Twitter:** @Minitab (5,017 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/39142/ (732 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Quality Manager
- **Top Industries:** Automotive, Manufacturing
- **Company Size:** 46% Enterprise, 32% Mid-Market


#### What Are Minitab Statistical Software's Pros and Cons?

**Pros:**

- Ease of Use (62 reviews)
- Data Analysis (53 reviews)
- Statistical Analysis (38 reviews)
- Analysis (32 reviews)
- Analysis Capabilities (30 reviews)

**Cons:**

- Expensive (23 reviews)
- Learning Curve (22 reviews)
- Complexity (13 reviews)
- Not User-Friendly (13 reviews)
- Data Management Issues (10 reviews)


### What Do G2 Reviewers Say About Minitab Statistical Software?
*AI-generated summary from verified user reviews*

**Pros:**

- Users praise the **ease of use** of Minitab, highlighting its intuitive interface and helpful resources for efficient analysis.
- Users praise Minitab for its **ease of use** , enhancing their overall data analysis experience significantly.
- Users value the **helpful guides and assistants** in Minitab, enhancing their data analysis experience significantly.
- Users value Minitab for its **comprehensive tools** that enhance data-driven decision-making in manufacturing and quality analysis.
- Users value Minitab's **robust analysis capabilities** , making statistical data processing straightforward and effective for decision-making.

**Cons:**

- Users find the **licensing fees expensive** , especially when usage is intermittent and features are underutilized.
- Users find a **steep learning curve** with Minitab, highlighting the need for better training resources and usability improvements.
- Users find Minitab's **complexity** challenging due to issues with hidden variables and clunky data manipulation features.
- Users find Minitab’s interface **not user-friendly** , as confusing menus and outdated design hinder ease of use.
- Users face **data management issues** in Minitab, complicating analysis and requiring re-runs with updated datasets.

#### What Are Recent G2 Reviews of Minitab Statistical Software?

**"[Audit-Ready Trend Analysis and Clear Statistical Outputs for APQR](https://www.g2.com/survey_responses/minitab-statistical-software-review-12736010)"**

**Rating:** 4.0/5.0 stars
*— Ajay K.*

[Read full review](https://www.g2.com/survey_responses/minitab-statistical-software-review-12736010)

---

**"[Minitab’s DOE Wizard Makes Complex Experiments Fast and Straightforward](https://www.g2.com/survey_responses/minitab-statistical-software-review-13070943)"**

**Rating:** 5.0/5.0 stars
*— Nikhil C.*

[Read full review](https://www.g2.com/survey_responses/minitab-statistical-software-review-13070943)

---


#### What Are G2 Users Discussing About Minitab Statistical Software?

- [What improvements would you suggest for Minitab to better meet your data analysis needs?](https://www.g2.com/discussions/what-improvements-would-you-suggest-for-minitab-to-better-meet-your-data-analysis-needs)
- [What is Minitab Statistical Software used for?](https://www.g2.com/discussions/what-is-minitab-statistical-software-used-for) - 1 comment
- [Is Minitab free for students?](https://www.g2.com/discussions/minitab-statistical-software-is-minitab-free-for-students) - 2 comments
- [Is Minitab free for students?](https://www.g2.com/discussions/is-minitab-free-for-students)
- [What Minitab 19?](https://www.g2.com/discussions/minitab-statistical-software-what-minitab-19)

### 23. [Jarvis](https://www.g2.com/products/nvidia-jarvis/reviews)
NVIDIA Jarvis is an application framework for multimodal conversational AI services that delivers real-time performance on GPUs.


**Average Rating:** 4.2/5.0
**Total Reviews:** 21
**How Do G2 Users Rate Jarvis?**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.4/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.8/10 (Category avg: 8.4/10)
- **Ease of Admin:** 8.3/10 (Category avg: 8.5/10)

**Who Is the Company Behind Jarvis?**

- **Seller:** [NVIDIA](https://www.g2.com/sellers/nvidia)
- **Year Founded:** 1993
- **HQ Location:** Santa Clara, CA
- **Twitter:** @nvidia (2,582,827 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3608/ (48,229 employees on LinkedIn®)
- **Ownership:** NVDA

**Who Uses This Product?**
- **Top Industries:** Computer Software
- **Company Size:** 74% Small-Business, 13% Enterprise



#### What Are Recent G2 Reviews of Jarvis?

**"[Fast, Reliable, and a Perfect Fit for MacOS](https://www.g2.com/survey_responses/jarvis-review-12458350)"**

**Rating:** 5.0/5.0 stars
*— E G.*

[Read full review](https://www.g2.com/survey_responses/jarvis-review-12458350)

---

**"[Easy to use, good price, and reliable support](https://www.g2.com/survey_responses/jarvis-review-13030794)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Logistics and Supply Chain*

[Read full review](https://www.g2.com/survey_responses/jarvis-review-13030794)

---



### 24. [B2Metric](https://www.g2.com/products/b2metric/reviews)
B2Metric is an AI/ML-powered data analytics platform that enables marketing, data analytics, and CRM teams to better understand customer trends and behaviors. B2Metric uses machine learning to automate data analysis and generate predictive insights, which can be used to improve customer engagement, retention, and growth.


**Average Rating:** 4.8/5.0
**Total Reviews:** 44
**How Do G2 Users Rate B2Metric?**

- **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.8/10 (Category avg: 8.5/10)
- **Quality of Support:** 9.7/10 (Category avg: 8.4/10)
- **Ease of Admin:** 9.8/10 (Category avg: 8.5/10)

**Who Is the Company Behind B2Metric?**

- **Seller:** [B2Metric](https://www.g2.com/sellers/b2metric)
- **Year Founded:** 2018
- **HQ Location:** Menlo Park, California
- **Twitter:** @B2Metric (228 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/b2metric (31 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software, Financial Services
- **Company Size:** 52% Small-Business, 30% Mid-Market


#### What Are B2Metric's Pros and Cons?

**Pros:**

- Problem Solving (4 reviews)
- Reporting Features (4 reviews)
- AI Technology (3 reviews)
- Data Visualization (3 reviews)
- Easy Integration (3 reviews)

**Cons:**

- Learning Curve (6 reviews)
- Complex Implementation (2 reviews)
- Difficult Learning (2 reviews)
- High Complexity (2 reviews)
- Insufficient Training (2 reviews)


### What Do G2 Reviewers Say About B2Metric?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value B2Metric for its **powerful problem-solving capabilities** , enabling efficient data-driven decisions and faster reporting.
- Users value the **efficient reporting features** of B2Metric, enhancing data access and accelerating insights for better decision-making.
- Users praise the **powerful AI-driven insights** of B2Metric, enabling effortless data-driven decision-making and enhanced efficiency.
- Users value the **streamlined data visualization** of B2Metric, simplifying data interpretation and enhancing actionable insights.
- Users commend B2Metric's **easy integration** , which simplifies connections and enhances workflow efficiency significantly.

**Cons:**

- Users find the **learning curve steep** , requiring extra time and guidance to master B2Metric's features effectively.
- Users find the **complex implementation** of B2Metric challenging, though customer support assists in overcoming these hurdles.
- Users find B2Metric's **difficult learning curve** can make mastering its features and functionalities challenging for newcomers.
- Users find the **high complexity** of B2Metric's numerous modules can make navigation and understanding challenging.
- Users find the **insufficient training** in B2Metric makes onboarding and understanding the platform challenging.

#### What Are Recent G2 Reviews of B2Metric?

**"[Fast, Smart and moreover, supported by Artificial Intelligence and Machine Learning!](https://www.g2.com/survey_responses/b2metric-review-10445187)"**

**Rating:** 5.0/5.0 stars
*— Zeynep E.*

[Read full review](https://www.g2.com/survey_responses/b2metric-review-10445187)

---

**"[Being CDP and AI/ML based at the same time is something else!](https://www.g2.com/survey_responses/b2metric-review-10688291)"**

**Rating:** 5.0/5.0 stars
*— Erman  K.*

[Read full review](https://www.g2.com/survey_responses/b2metric-review-10688291)

---



### 25. [Aerosolve](https://www.g2.com/products/aerosolve/reviews)
Aerosolve is a machine learning package built for humans its library is meant to be used with sparse, interpretable features such as those that commonly occur in search (search keywords, filters) or pricing (number of rooms, location, price). It is not as interpretable with problems with very dense non-human interpretable features such as raw pixels or audio samples.


**Average Rating:** 4.5/5.0
**Total Reviews:** 17
**How Do G2 Users Rate Aerosolve?**

- **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.4/10 (Category avg: 8.5/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.4/10)
- **Ease of Admin:** 10.0/10 (Category avg: 8.5/10)

**Who Is the Company Behind Aerosolve?**

- **Seller:** [Airbnb](https://www.g2.com/sellers/airbnb)
- **Year Founded:** 2007
- **HQ Location:** San Francisco, CA
- **Twitter:** @Airbnb (843,260 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/309694/ (68,394 employees on LinkedIn®)
- **Ownership:** NASDAQ: ABNB

**Who Uses This Product?**
- **Top Industries:** Computer Software
- **Company Size:** 67% Small-Business, 28% Mid-Market



#### What Are Recent G2 Reviews of Aerosolve?

**"[Aerosolve with its best features](https://www.g2.com/survey_responses/aerosolve-review-9988783)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Leisure, Travel & Tourism*

[Read full review](https://www.g2.com/survey_responses/aerosolve-review-9988783)

---

**"[Aerosolve is the best and most smooth system.](https://www.g2.com/survey_responses/aerosolve-review-9123659)"**

**Rating:** 5.0/5.0 stars
*— Sagar M.*

[Read full review](https://www.g2.com/survey_responses/aerosolve-review-9123659)

---




## What Is Machine Learning Software?

[Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence)

## What Software Categories Are Similar to Machine Learning Software?

- [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics)
- [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms)
- [MLOps Platforms](https://www.g2.com/categories/mlops-platforms)


---

## How Do You Choose the Right Machine Learning Software?

### What You Should Know About Machine Learning Software

### Machine learning software buying insights at a glance

[Machine learning software](https://www.g2.com/categories/machine-learning) helps organizations transform large volumes of raw data into meaningful predictions and insights. As companies collect increasing amounts of operational, customer, and behavioral data, traditional analytics tools often fall short in identifying deeper patterns or forecasting future outcomes. By using algorithms that learn from historical data, top machine learning tools enable businesses to uncover trends, anticipate risks, and automate complex decision-making processes, without manual intervention.

When evaluating the best machine learning software, buyers typically look for platforms that make it easier to move from experimentation to production. These tools allow data scientists and engineers to train models on large datasets, deploy them into real-world applications, and monitor their performance over time. The best machine learning platforms also simplify collaboration across teams, enabling analysts, developers, and operations leaders to work from a single environment.

Across industries, organizations use machine learning software to solve a wide range of business challenges. Some of the most common use cases include predictive analytics for demand forecasting, churn prediction, and revenue planning; fraud detection and anomaly detection in financial and cybersecurity workflows; recommendation engines for [e-commerce platforms](https://www.g2.com/categories/e-commerce-platforms) and streaming services; natural language processing for [chatbots](https://www.g2.com/categories/chatbots) and automated support tools; image recognition and document classification for operational automation

Pricing for machine learning platforms varies significantly depending on the level of compute power, data processing, and automation features required. Many cloud-based solutions operate on consumption-based pricing tied to compute usage and storage, while enterprise platforms may offer subscription-based licensing alongside infrastructure costs.

### Top 5 FAQs from software buyers:

- How does machine learning differ from [artificial intelligence](https://www.g2.com/categories/artificial-intelligence) (AI) and [deep learning](https://www.g2.com/categories/deep-learning)?
- How does the machine learning software integrate with my existing data and infrastructure?
- How is the machine learning model’s accuracy calculated and validated?
- What post-deployment support is included for machine learning maintenance and monitoring?

G2’s top-rated machine learning software, based on verified user reviews, includes [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews), [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews), [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews), [Google Cloud TPU](https://www.g2.com/products/google-cloud-tpu/reviews), and [AIToolbox](https://www.g2.com/products/aitoolbox/reviews). ([Source 2](https://www.g2.com/reports))

### What are the top-reviewed machine learning software on G2?

[Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews)

- Reviews: 328
- Satisfaction: 98
- Market Presence: 98
- G2 Score: 98

[IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews)

- Reviews: 47
- Satisfaction: 85
- Market Presence: 89
- G2 Score: 87

[SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)

- Reviews: 90
- Satisfaction: 83
- Market Presence: 75
- G2 Score: 79

[Google Cloud TPU](https://www.g2.com/products/google-cloud-tpu/reviews)

- Reviews: 18
- Satisfaction: 78
- Market Presence: 66
- G2 Score: 72

[AIToolbox](https://www.g2.com/products/aitoolbox/reviews)

- Reviews: 15
- Satisfaction: 80
- Market Presence: 64
- G2 Score: 72

**Satisfaction** reflects user-reported ratings across factors such as ease of use, feature fit, and quality of support. ([Source 2](https://www.g2.com/reports))

**Market Presence** scores combine review volume, third-party signals, and overall market visibility. ([Source 2](https://www.g2.com/reports))

**G2 Score** is a weighted composite of Satisfaction and Market Presence. ([Source 2](https://www.g2.com/reports))

Learn how G2 scores products. ([Source 1](https://documentation.g2.com/docs/research-scoring-methodologies))

### What I Often See in Machine Learning Software?

#### Feedback Pros: What Users Consistently Appreciate

- **Unified platform covering training, deployment, and monitoring workflows**
- “I use Vertex AI for building, training, and deploying machine learning models, and I love how it solves the problem of managing complex ML workflows. It reduces the effort required to build, train, and deploy models by centralizing everything, making automation easier and scaling faster. This means I can focus more on building better models instead of worrying about infrastructure. What I like most is how it combines training, deployment, and monitoring in one place. The integration with Google Cloud services works really well, scaling is smooth, and managed pipelines save a lot of time. Overall, it makes ML development more efficient and reliable.” - [Jeni J](https://www.g2.com/products/google-vertex-ai/reviews/vertex-ai-review-12264823), Vertex AI Review
- **Strong cloud integrations supporting scalable model training and pipelines**
- “What I like most about SAS Viya is its cloud-native architecture and strong performance. It enables faster data processing through in-memory analytics, supports Python, R, and SQL alongside SAS, and offers convenient access via a web-based interface. Overall, these capabilities make analytics more scalable, collaborative, and flexible than in traditional SAS environments.” - [Sachin M](https://www.g2.com/products/sas-sas-viya/reviews/sas-viya-review-12320006), SAS Viya Review
- **User-friendly interfaces simplifying experimentation with machine learning models**
- “I find IBM watsonx.ai impressive because it's not just a model playground; it’s built for real enterprise use. I love that it solves practical, real-world business problems by making AI easier to build, manage, and trust. The platform supports everything from data prep and model training to tuning and development. It effectively blends capabilities from traditional machine learning workflows with generative AI tools into a single platform, helping enterprises operationalize AI faster. I also appreciate how easy the initial setup is.” - [Marilyn B](https://www.g2.com/products/ibm-watsonx-ai/reviews/ibm-watsonx-ai-review-12381718), IBM watsonx.ai Review

#### Cons: Where Many Platforms Fall Short

- **Steep learning curve when configuring machine learning environments**
- “One area that could be improved is the learning curve for new users, especially when configuring services in Google Cloud. Pricing and documentation could also be clearer for beginners.” - [Syed Shariq A](https://www.g2.com/products/google-vertex-ai/reviews/vertex-ai-review-12447891), Vertex AI Review
- **Unpredictable pricing tied to compute-heavy model training workloads**
- “One potential downside of SAS Viya is that it can have a steep learning curve, especially for users who are new to SAS or enterprise analytics platforms. The cost of licensing and implementation can also be high compared with some open-source alternatives, which may limit accessibility for smaller organizations. Additionally, while Viya supports multiple programming languages, some advanced customization can still feel more seamless within the SAS ecosystem, which may reduce flexibility for teams that primarily work in open-source environments.” - [John M](https://www.g2.com/products/sas-sas-viya/reviews/sas-viya-review-12324695), SAS Viya Review
- **Debugging pipelines and monitoring distributed model performance remains difficult**
- “One downside of Google Cloud TPU is that it’s more specialized than GPUs, so it tends to work best with TensorFlow and a limited set of supported frameworks. This can reduce flexibility if your team relies on multiple machine learning frameworks across different projects. Debugging and monitoring TPU workloads can also be more complicated than with traditional GPU setups, which may add friction during development and troubleshooting. In addition, costs can add up quickly for long-running training jobs if resources aren’t optimized and managed carefully.” -  [Mahmoud H](https://www.g2.com/products/google-cloud-tpu/reviews/google-cloud-tpu-review-12271918), Google Cloud TPU Review

### My Expert Takeaway on Machine Learning Software in 2026

88% of G2 reviewers mentioned they are likely to recommend their machine learning software. The top-rated tools also earned high marks for ease of use (avg. 88%) and ease of setup (avg. 86%), especially among SMBs and mid-market teams looking to use these machine learning tools to scale predictive models more efficiently. 

High-performing organizations treat machine learning platforms as part of a broader data ecosystem rather than standalone tools. High-performing teams, especially in industries such as fintech, ecommerce, and SaaS, often integrate machine learning directly into their analytics pipelines, data warehouses, and production applications. This allows predictions to run continuously in the background of operational systems.

G2 reviewers frequently emphasize that even the best machine learning software requires thoughtful implementation. Companies that see the strongest results typically invest in data engineering, MLOps practices, and cross-team collaboration between data scientists and software engineers. When those pieces come together, the best machine learning platforms can dramatically accelerate experimentation and turn predictive insights into everyday business decisions.

### Machine Learning Software FAQs

#### **What is the most cost-efficient machine learning platform?**

Cost efficiency depends on workload size and pricing structure. [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) primarily uses usage-based pricing tied to compute and predictions, while [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews)offers both pay-as-you-go and subscription tiers. [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) is typically sold through enterprise subscriptions depending on deployment needs.

#### **What is the most secure machine learning platform for sensitive data?**

Platforms such as [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews) and [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) emphasize governance, access controls, and compliance features. [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) and [Google Cloud TPU](https://www.g2.com/products/google-cloud-tpu/reviews) also rely on built-in cloud security frameworks.

#### **What is the top ML platform for enterprise AI development?**

Enterprise teams often use platforms like [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews), [AI Toolbox](https://www.g2.com/products/aitoolbox/reviews), and [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews) because they combine model development, deployment, and governance in one environment.

#### **What ML software offers the easiest model deployment process?**

Platforms such as [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) and [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) provide managed pipelines and deployment tools that simplify moving models from experimentation to production.

#### **What platform is best for real-time ML predictions?**

Real-time prediction workloads often use platforms like [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) for scalable endpoints and [Google Cloud TPU](https://www.g2.com/products/google-cloud-tpu/reviews) for high-performance inference.

#### **Which machine learning platform offers the best predictive analytics tools?**

Platforms such as [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews), [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews), and [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews) provide strong predictive analytics capabilities, including model training, evaluation, and monitoring tools.

### Sources

[G2 Scoring Methodologies](https://documentation.g2.com/docs/research-scoring-methodologies)

[G2 Winter Reports](https://www.g2.com/reports)

Researched by [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)

Last Updated on March 17, 2026




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## What Are the Most Common Questions About Machine Learning Software?

### What are the key features to look for in a Machine Learning platform?

Key features to look for in a Machine Learning platform include robust data integration capabilities, user-friendly interfaces for model building, automated machine learning (AutoML) functionalities, strong support for various algorithms, scalability options, and comprehensive analytics and reporting tools. Additionally, platforms that offer collaboration features and extensive documentation tend to receive higher user satisfaction ratings, enhancing the overall user experience.



### How does pricing typically vary among Machine Learning solutions?

Pricing for Machine Learning solutions varies significantly based on features and deployment options. For instance, products like DataRobot and H2O.ai typically offer tiered pricing models, with entry-level plans starting around $1,000 per month, while more advanced solutions can exceed $10,000 monthly. Other solutions, such as Google Cloud AI and Microsoft Azure Machine Learning, often utilize a pay-as-you-go model, where costs depend on usage metrics like compute time and data processed. Overall, users can expect a range from free tiers to enterprise-level pricing, reflecting the diverse needs of organizations.



### What are common use cases for Machine Learning in my industry?

Common use cases for Machine Learning include predictive analytics, where businesses forecast trends and behaviors; natural language processing for chatbots and sentiment analysis; image recognition in security and healthcare; and recommendation systems in e-commerce. Products like DataRobot, H2O.ai, and Google Cloud AI are frequently utilized for these applications, with users highlighting their effectiveness in automating data-driven decision-making and enhancing customer experiences.



### What integrations should I consider for my Machine Learning projects?

For Machine Learning projects, consider integrations with platforms like TensorFlow, which is highly rated for its flexibility and extensive community support. Apache Spark is also popular for its ability to handle large-scale data processing. Additionally, look into integration with cloud services like AWS and Google Cloud, which provide robust machine learning tools and infrastructure. Other notable mentions include Microsoft Azure for its comprehensive suite of AI services and Jupyter Notebooks for interactive data science and machine learning workflows.



### How scalable are most Machine Learning solutions for growing businesses?

Most Machine Learning solutions are designed to be highly scalable for growing businesses. For instance, products like DataRobot and H2O.ai are frequently praised for their ability to handle increasing data volumes and user demands, with users noting their flexibility in deployment across various environments. Additionally, platforms such as Google Cloud AI and Microsoft Azure Machine Learning offer robust scalability features, allowing businesses to expand their usage seamlessly as their needs evolve. Overall, user feedback indicates that scalability is a key strength of many leading Machine Learning solutions.



### What level of technical expertise is required to implement Machine Learning tools?

Implementing Machine Learning tools typically requires a moderate to high level of technical expertise. Users often report that familiarity with programming languages such as Python or R, as well as knowledge of data science concepts, is essential. For instance, platforms like DataRobot and H2O.ai are noted for their user-friendly interfaces, which can lower the barrier for entry, while tools like TensorFlow and PyTorch demand more advanced skills. Overall, the complexity of the tool and the specific use case significantly influence the required expertise.



### How do user experiences differ across popular Machine Learning platforms?

User experiences across popular Machine Learning platforms like TensorFlow, PyTorch, and H2O.ai vary significantly. TensorFlow users often highlight its extensive community support and comprehensive documentation, rating it highly for scalability and deployment capabilities. In contrast, PyTorch is favored for its ease of use and flexibility, particularly among researchers, leading to higher satisfaction in prototyping. H2O.ai users appreciate its automated machine learning features, which streamline model building, although some note a steeper learning curve. Overall, TensorFlow excels in production environments, while PyTorch is preferred for research and experimentation.



### What kind of customer support is generally available for Machine Learning software?

Customer support for Machine Learning software typically includes options such as email support, live chat, and extensive documentation. For instance, products like DataRobot and H2O.ai offer robust customer support with high ratings for responsiveness. Additionally, many platforms provide community forums and knowledge bases, enhancing user assistance. Some vendors, like IBM Watson, also offer dedicated account management for enterprise clients, ensuring tailored support. Overall, the availability and quality of support can vary significantly across different software solutions.



### How do I evaluate the performance of different Machine Learning algorithms?

To evaluate the performance of different Machine Learning algorithms, consider metrics such as accuracy, precision, recall, and F1 score, which are commonly highlighted in user reviews. For instance, users of TensorFlow often praise its flexibility and extensive community support, while those using Scikit-learn appreciate its simplicity and effectiveness for smaller datasets. Additionally, PyTorch users frequently mention its dynamic computation graph as a key advantage for research purposes. Comparing these metrics and user experiences can provide insights into the best algorithm for your specific needs.



### What are the data security considerations when using Machine Learning tools?

When using Machine Learning tools, data security considerations include ensuring compliance with data protection regulations, implementing robust encryption methods, and managing access controls effectively. Users frequently highlight the importance of data anonymization and secure data storage practices. Tools like DataRobot, H2O.ai, and RapidMiner are noted for their strong security features, including user authentication and audit trails, which help mitigate risks associated with data breaches. Additionally, many users emphasize the need for regular security assessments and updates to maintain the integrity of sensitive data.



### How do Machine Learning solutions handle data privacy and compliance?

Machine Learning solutions prioritize data privacy and compliance through features such as data encryption, user access controls, and compliance certifications. For instance, products like DataRobot and H2O.ai emphasize GDPR compliance and provide tools for data anonymization. Additionally, platforms like IBM Watson and Google Cloud AI offer robust security measures and compliance frameworks, ensuring that user data is handled according to legal standards. User reviews highlight the importance of these features, with many users noting the effectiveness of these solutions in maintaining data integrity and privacy.



### What are the typical implementation timelines for Machine Learning projects?

Implementation timelines for Machine Learning projects typically range from 3 to 12 months, depending on project complexity and organizational readiness. For instance, platforms like DataRobot and H2O.ai report average timelines of 6 to 9 months for initial deployment, while TensorFlow users often cite longer timelines due to customization needs. Additionally, user feedback indicates that smaller projects can be implemented in as little as 3 months, while larger, more integrated solutions may take up to a year or more.