# Best  Low-Code Machine Learning Platforms Software

*By [Adam Crivello](https://research.g2.com/insights/author/adam-crivello)*


Low-code machine learning (ML) platforms enable businesses to build, train, and deploy ML models primarily through visual or guided interfaces, using drag-and-drop tools, AutoML workflows, and wizard-style guidance to make predictive modeling and AI development accessible to business analysts, subject matter experts, and data scientists without extensive coding expertise.

### Core Capabilities of Low-Code Machine Learning Platforms

To qualify for inclusion in the Low-Code Machine Learning (ML) Platforms category, a product must:

- Provide a graphical, low-code or no-code interface to build and train custom ML models on user-provided data
- Include built-in functionality to evaluate trained models
- Offer direct deployment options from the interface, such as batch scoring, API endpoints, or managed service environments
- Support data ingestion through uploads or connectors to databases, cloud storage, or other sources
- Enable collaboration and governance through features like role-based access, project or workspace management, or auditability

### Common Use Cases for Low-Code Machine Learning Platforms

Business analysts, data scientists, and non-technical teams use low-code ML platforms to accelerate AI adoption without deep programming expertise. Common use cases include:

- Building and deploying predictive models for use cases such as churn prediction, demand forecasting, and fraud detection
- Empowering non-technical subject matter experts to contribute to ML model development using visual interfaces
- Standardizing the deployment and governance of ML models into production environments across the enterprise

### How Low-Code Machine Learning Platforms Differ from Other Tools

Unlike traditional [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), which require extensive programming and are primarily designed for experienced data scientists, low-code ML platforms deliver end-to-end ML lifecycle functionality through a user-friendly interface. Some enterprise cloud providers offer low-code ML capabilities within broader AI ecosystems, while dedicated vendors focus solely on visual model development and deployment.

### Insights from G2 on Low-Code Machine Learning Platforms

Based on category trends on G2, the visual model builder and AutoML capabilities stand out as standout features. These platforms deliver faster time-to-model deployment and reduced dependency on data science resources as primary benefits of adoption.






## G2 Grid® for  Low-Code Machine Learning Platforms Software
![G2 Grid® for  Low-Code Machine Learning Platforms Software plotting products by satisfaction and market presence](https://www.g2.com/categories/low-code-machine-learning-platforms/grids.png?focus%5B%5D=1327283&focus%5B%5D=7150&focus%5B%5D=989&focus%5B%5D=21469&focus%5B%5D=16291&focus%5B%5D=38300&focus%5B%5D=16295)
Highlighted products: SAS Viya, Dataiku, Alteryx, Gemini Enterprise Agent Platform, KNIME, Qlik Predict, and Altair AI Studio.
Underlying data: [Grid® JSON](https://www.g2.com/categories/low-code-machine-learning-platforms/grids.json?focus%5B%5D=sas-sas-viya&focus%5B%5D=dataiku&focus%5B%5D=alteryx&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=knime-analytics-platform&focus%5B%5D=qlik-predict&focus%5B%5D=rapidminer-studio)


## How Many  Low-Code Machine Learning Platforms Software Products Does G2 Track?
**Total Products under this Category:** 21

### Category Stats (Jul 2026)
- **Average Rating**: 4.39/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product**: KNIME (+1.31%) - Among all products in this category, KNIME recorded the largest rating increase compared to last month
*Last updated: July 12, 2026*


## How Does G2 Rank  Low-Code Machine Learning Platforms Software Products?

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

- 30 Analysts and Data Experts
- 3,700+ Authentic Reviews
- 21+ 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  Low-Code Machine Learning Platforms Software Is Best for Your Use Case?

- **Leader:** [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)
- **Easiest to Use:** [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)
- **Top Trending:** [Dataiku](https://www.g2.com/products/dataiku/reviews)
- **Best Free Software:** [Alteryx](https://www.g2.com/products/alteryx/reviews)


---

**Sponsored**

### Alteryx

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.



[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=1011941&secure%5Bchosen_at%5D=2026-07-12T05%3A01%3A37Z&secure%5Bdisplayable_resource_id%5D=1011941&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1011941&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=989&secure%5Bresource_id%5D=1011941&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Flow-code-machine-learning-platforms%3Fopen_modal_url%3D%252Fproducts%252Fibm-watsonx-ai%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Flow-code-machine-learning-platforms%2526source%253Dcategory&secure%5Btoken%5D=478c3e14820a7930501d688b9ab010ebe434d94a690c0a9ffd6a3ab171c032cb&secure%5Burl%5D=https%3A%2F%2Fwww.alteryx.com%2Ftrial%3Futm_source%3Dg2%26utm_medium%3Dreviewsite%26utm_campaign%3DFY25_Global_AllRegions_AlwaysOn_AllPersonas_IndustryAgnostic%26utm_content%3Dg2_freetrial&secure%5Burl_type%5D=free_trial)

---

## What Are the Top-Rated  Low-Code Machine Learning Platforms Software Products in 2026?
### 1. [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:** 764

**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, Statistical Programmer
- **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)
- User Interface (147 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** in SAS Viya, enabling effortless data visualization and enhancing decision-making efficiency.
- Users value the **advanced analytical capabilities** of SAS Viya, enhancing decision-making with a user-friendly interface.
- Users appreciate the **sophisticated analytical methods** in SAS Viya, enhancing decision-making and providing valuable business insights.
- Users value the **end-to-end data lifecycle tooling** of SAS Viya, enhancing insights and streamlining logistic data analysis.
- Users value the **user-friendly interface** of SAS Viya, making complex analytics accessible to all skill levels.

**Cons:**

- Users find it **difficult for non-technical users** to navigate reports and dashboards, impacting their overall experience.
- Users find the **learning curve challenging** , especially non-technical users struggling to access reports and dashboards.
- Users find the **complexity of visualization** in SAS Viya challenging, especially for newcomers and non-technical individuals.
- Users find the **difficult learning curve** of SAS Viya challenging, particularly for new and non-technical users.
- Users find the pricing of SAS Viya to be **expensive** and often confusing without extensive communication.

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

**"[Powerful & Transforming Data into Decisions—Effortlessly and Intelligently.](https://www.g2.com/survey_responses/sas-viya-review-12682824)"**

**Rating:** 4.5/5.0 stars
*— Venkatesh D.*

[Read full review](https://www.g2.com/survey_responses/sas-viya-review-12682824)

---

**"[Intuitive Interface with Fast, Practical Reporting for Massive Data](https://www.g2.com/survey_responses/sas-viya-review-13091171)"**

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

[Read full review](https://www.g2.com/survey_responses/sas-viya-review-13091171)

---


#### 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

### 2. [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:** 210

**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, 23% Mid-Market


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

**Pros:**

- Ease of Use (82 reviews)
- Features (82 reviews)
- Usability (46 reviews)
- Easy Integrations (43 reviews)
- Productivity Improvement (42 reviews)

**Cons:**

- Learning Curve (45 reviews)
- Steep Learning Curve (26 reviews)
- Slow Performance (24 reviews)
- Difficult Learning (23 reviews)
- Expensive (22 reviews)


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

**Pros:**

- Users love the **ease of use** of Dataiku, finding it simplifies ML development and data integration.
- Users appreciate the **easy learning curve and comprehensive training resources** of Dataiku, enhancing their ML development experience.
- Users appreciate the **user-friendly and intuitive interface** of Dataiku, making data collaboration and analysis effortless.
- Users appreciate the **easy integrations** in Dataiku, enabling seamless connections to various data sources and platforms.
- Users value the **productivity improvements** from Dataiku's centralized environment and intuitive, user-friendly features.

**Cons:**

- Users find the **learning curve steep** , especially for advanced features, complicating the experience for beginners.
- Users find the **steep learning curve** of Dataiku daunting, especially for beginners seeking to use advanced features.
- Users experience **slow performance** with Dataiku, particularly when handling large datasets and complex scenarios.
- Users find that acquiring necessary knowledge for Dataiku's advanced features can be a **difficult learning** experience.
- Users find the **high licensing costs** of Dataiku to be a significant barrier for smaller teams and organizations.

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

**"[From idea to model in minutes: Dataiku accelerates the team's work](https://www.g2.com/survey_responses/dataiku-review-12967713)"**

**Rating:** 4.5/5.0 stars
*— Bill C.*

[Read full review](https://www.g2.com/survey_responses/dataiku-review-12967713)

---

**"[Dataiku: No-Code ETL Powerhouse — Collaborative, Visual, and Python/SQL Friendly](https://www.g2.com/survey_responses/dataiku-review-13046146)"**

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

[Read full review](https://www.g2.com/survey_responses/dataiku-review-13046146)

---


#### 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)

### 3. [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

**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 love the **user-friendly interface** of Alteryx, making it easy for anyone to efficiently complete tasks.
- Users appreciate the **automation capabilities** of Alteryx, enhancing speed and efficiency in data processing tasks.
- Users find Alteryx **very intuitive and easy to learn** , making technology accessible for non-technical individuals.
- Users find Alteryx to be **very easy to learn and use** , enhancing their workflow automation and data analytics experience.
- Users value the **efficiency** of Alteryx, effortlessly managing and processing data for streamlined analysis and reporting.

**Cons:**

- Users find the **pricing excessively high** , making it challenging for small teams and startups to afford.
- Users face a **steep learning curve** with Alteryx, requiring time to master its advanced features and operations.
- Users highlight the **missing features** in Alteryx, notably lacking good reporting and direct database access.
- Users find the **learning difficulty** of Alteryx challenging, especially for those not familiar with RegEx or SQL.
- Users experience **slow performance** with Alteryx, especially during analysis of large workflows, impacting efficiency.

#### 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)

---



### 4. [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

**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 find the **ease of use** of Gemini Enterprise Agent Platform greatly enhances productivity and workflow efficiency.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity by integrating machine learning tasks into a single platform.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity in software development and automation projects.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity and streamlining software development and automation projects.
- Users value the **integrated platform** of Gemini for unifying workflows and enhancing productivity across various tasks.

**Cons:**

- Users find the **pricing elusive** , leading to unexpected bills if resource usage isn't closely monitored.
- Users find the **complexity of Vertex AI** overwhelming, especially when navigating settings and advanced features.
- Users find the **learning curve challenging** initially, particularly with resource organization and complex features in Gemini.
- Users find the **complexity of settings** and steep learning curve of Gemini Enterprise Agent Platform challenging and costly.
- Users find the **difficult learning curve** of Gemini Enterprise Agent Platform challenging, especially for new and inexperienced users.

#### 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

### 5. [KNIME](https://www.g2.com/products/knime-analytics-platform/reviews)
KNIME helps everybody make sense of data. Its free and open source KNIME Analytics Platform enables anyone — whether they come from a business, technical or data background — to intuitively work with data, every day. KNIME Business Hub is the commercial complement to KNIME Analytics Platform and enables users to collaborate on data science and share insights across the organization. Together, the products support the complete data science lifecycle, allowing teams at all levels of analytics readiness to support the operationalization of data and to build a scalable data science practice.


**Average Rating:** 4.5/5.0
**Total Reviews:** 98

**Who Is the Company Behind KNIME?**

- **Seller:** [KNIME](https://www.g2.com/sellers/knime)
- **Company Website:** https://knime.com
- **Year Founded:** 2008
- **HQ Location:** Zurich, Switzerland
- **Twitter:** @knime (7,998 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/692207?trk=tyah&trkInfo=clickedVertical%3Acompany%2CclickedEntityId%3A692207%2Cidx%3A2-1-4%2CtarId%3A1454002156993%2Ctas%3Aknime (244 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services, Higher Education
- **Company Size:** 43% Enterprise, 33% Mid-Market


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

**Pros:**

- Ease of Use (7 reviews)
- Coding Ease (4 reviews)
- Ease of Learning (4 reviews)
- Learning (4 reviews)
- Data Visualization (3 reviews)

**Cons:**

- Learning Difficulty (3 reviews)
- Memory Usage (3 reviews)
- Storage Limitations (3 reviews)
- Data Management Issues (2 reviews)
- Insufficient Learning Resources (2 reviews)


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

**Pros:**

- Users find KNIME **extremely easy to use** , appreciating its intuitive interface and point-and-click workflow capabilities.
- Users appreciate the **coding ease** of KNIME, allowing non-coders to build powerful workflows effortlessly.
- Users appreciate the **ease of learning** with KNIME, allowing beginners to quickly start delivering results.
- Users find KNIME to be **easy to learn** , enabling quick delivery of data solutions and enhancing workflow efficiency.
- Users praise the **user-friendly data visualization tools** of KNIME, which simplify complex analyses for everyone.

**Cons:**

- Users find the **initial learning curve challenging** , especially for those new to data science or visual programming.
- Users experience **memory availability issues** with KNIME, especially when handling large files, impacting performance and efficiency.
- Users often face **storage limitations** with KNIME, experiencing slow performance and memory issues, especially with large datasets.
- Users find that **data management issues** hinder their experience, particularly with file handling and database readability.
- Users find the **lack of learning resources** frustrating, impacting their ability to fully utilize KNIME's features.

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

**"[KNIME’s Free No-Code Drag-and-Drop Analytics, from Descriptive to Agentic AI](https://www.g2.com/survey_responses/knime-review-12992618)"**

**Rating:** 5.0/5.0 stars
*— Guylaine B.*

[Read full review](https://www.g2.com/survey_responses/knime-review-12992618)

---

**"[KNIME’s Visual Workflows - One of the best tool for Auditing, Accounting & Finance Professionals](https://www.g2.com/survey_responses/knime-review-12976842)"**

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

[Read full review](https://www.g2.com/survey_responses/knime-review-12976842)

---


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

- [What is KNIME Analytics Platform used for?](https://www.g2.com/discussions/what-is-knime-analytics-platform-used-for)
- [Is Knime easy to use?](https://www.g2.com/discussions/is-knime-easy-to-use) - 1 comment
- [How do I use Knime Analytics?](https://www.g2.com/discussions/how-do-i-use-knime-analytics)
- [Is Knime any good?](https://www.g2.com/discussions/is-knime-any-good)
- [What is Knime analytics platform?](https://www.g2.com/discussions/what-is-knime-analytics-platform)

### 6. [Qlik Predict](https://www.g2.com/products/qlik-predict/reviews)
Qlik AutoML (automated machine learning) brings AI-generated machine learning models and predictive analytics directly to your organization’s larger community of analytics users and teams, in a simple user experience focused on augmenting their intuition through machine intelligence. With AutoML, you can easily generate machine learning models, make predictions, and plan decisions – all within an intuitive, code-free user interface. Machine learning (ML) is a branch of artificial intelligence (AI) focused on the process of recognizing patterns in historical data to predict outcomes in the future. ML uses historically observed data as an input, applies a mathematical process against that data, and creates an output called a machine learning model based on patterns in historical data. This model can then be used to make future predictions and test scenarios.


**Average Rating:** 4.4/5.0
**Total Reviews:** 78

**Who Is the Company Behind Qlik Predict?**

- **Seller:** [Qlik](https://www.g2.com/sellers/qlik)
- **Year Founded:** 1993
- **HQ Location:** Radnor, PA
- **Twitter:** @qlik (64,130 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10162/ (4,551 employees on LinkedIn®)
- **Phone:** 1 (888) 994-9854

**Who Uses This Product?**
- **Who Uses This:** Data Analyst
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 38% Enterprise, 31% Small-Business


#### What Are Qlik Predict's Pros and Cons?

**Pros:**

- Automation (5 reviews)
- Ease of Use (5 reviews)
- AI Integration (4 reviews)
- Machine Learning (4 reviews)
- AI Capabilities (3 reviews)

**Cons:**

- Limited Customization (4 reviews)
- Deployment Issues (2 reviews)
- Lacking Features (2 reviews)
- Required Knowledge (2 reviews)
- Tool Limitations (2 reviews)


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

**Pros:**

- Users appreciate the **automation features** of Qlik Predict, enabling efficient and intuitive model building without technical expertise.
- Users appreciate the **ease of use** with Qlik Predict, enjoying a friendly interface and no-code implementation for projects.
- Users appreciate the **intuitive AI integration** of Qlik Predict, enabling quick and effortless predictive model development.
- Users commend the **intuitive machine learning interface** of Qlik Predict, enabling quick model creation without extensive coding skills.
- Users value the **user-friendly AI capabilities** of Qlik Predict, enabling seamless predictive analytics without coding skills.

**Cons:**

- Users find the **limited customization** options of Qlik Predict restricting, particularly for advanced model development needs.
- Users experience **deployment issues** due to limited flexibility and integration with external workflows beyond Qlik Sense.
- Users note a **lack of customization features** in Qlik Predict, limiting flexibility and scalability for advanced tasks.
- Users find the **required knowledge of data science** an obstacle to effectively utilizing Qlik Predict's automation features.
- Users find the **tool limitations** of Qlik Predict restrict flexibility and control, affecting customization for advanced users.

#### What Are Recent G2 Reviews of Qlik Predict?

**"[Qlik AutoML](https://www.g2.com/survey_responses/qlik-predict-review-11001365)"**

**Rating:** 4.0/5.0 stars
*— Anju P.*

[Read full review](https://www.g2.com/survey_responses/qlik-predict-review-11001365)

---

**"[Describe your experience in one short sentence.](https://www.g2.com/survey_responses/qlik-predict-review-11007278)"**

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

[Read full review](https://www.g2.com/survey_responses/qlik-predict-review-11007278)

---


#### What Are G2 Users Discussing About Qlik Predict?

- [How does the NZXT Kraken work?](https://www.g2.com/discussions/how-does-the-nzxt-kraken-work)
- [Is the NZXT Kraken water cooling?](https://www.g2.com/discussions/is-the-nzxt-kraken-water-cooling)
- [Which NZXT Kraken is the best?](https://www.g2.com/discussions/which-nzxt-kraken-is-the-best)
- [Does the NZXT Kraken come with fans?](https://www.g2.com/discussions/does-the-nzxt-kraken-come-with-fans)

### 7. [Altair AI Studio](https://www.g2.com/products/rapidminer-studio/reviews)
Altair AI Studio (formerly RapidMiner Studio) is a data science tool that anyone can use to design and prototype highly explainable AI and machine learning models that help build trust throughout an organization. Altair AI Studio includes: - Full generative AI functionality with access to hundreds of large language models (LLMs). - Intuitive and powerful drag-and-drop canvases that give users code-like control without complexity. - Award-winning auto ML with automated clustering, predictive modeling, feature engineering, and time series forecasting. - Data connectivity, exploration, and preparation. - Deploy and manage AI projects and models at enterprise scale. - Collaborate with team members in the same environment without having to worry about overwriting each other's work. - Unify the entire data science lifecycle from data exploration and machine learning to model operations and visualization and deploy in the cloud. Altair AI Studio helps users make powerful insights accessible to the entire organization and can scale seamlessly for users and enterprises. Altair AI studio enables organizations to derive significant value from AI with minimal cost and operational impact.


**Average Rating:** 4.6/5.0
**Total Reviews:** 494

**Who Is the Company Behind Altair AI Studio?**

- **Seller:** [Altair](https://www.g2.com/sellers/altair-186799f5-3238-493f-b3ad-b8cac484afd7)
- **Company Website:** https://www.altair.com/
- **Year Founded:** 1985
- **HQ Location:** Troy, MI
- **LinkedIn® Page:** https://www.linkedin.com/company/8323/ (2,774 employees on LinkedIn®)
- **Ownership:** NASDAQ:ALTR

**Who Uses This Product?**
- **Who Uses This:** Student, Data Scientist
- **Top Industries:** Higher Education, Education Management
- **Company Size:** 42% Small-Business, 30% Mid-Market


#### What Are Altair AI Studio's Pros and Cons?

**Pros:**

- Ease of Use (9 reviews)
- Machine Learning (8 reviews)
- AI Integration (6 reviews)
- AI Technology (5 reviews)
- Automation (5 reviews)

**Cons:**

- Complexity (4 reviews)
- Large Dataset Handling (3 reviews)
- Slow Performance (3 reviews)
- Complexity Issues (2 reviews)
- Complex Usage (2 reviews)


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

**Pros:**

- Users enjoy the **easy-to-use interface** of Altair AI Studio, allowing for seamless data science task completion.
- Users value the **no-code machine learning capability** of Altair AI Studio, facilitating easy model creation and advanced analytics.
- Users appreciate the **seamless AI integration** of Altair AI Studio, enhancing decision-making and efficiency in data analysis.
- Users admire the **advanced machine learning and data analytics** in Altair AI Studio, enhancing decision-making and efficiency.
- Users value the **automation capabilities** of Altair AI Studio, significantly enhancing efficiency and decision-making processes.

**Cons:**

- Users find the **complexity** of Altair AI Studio challenging, especially with language support and handling large datasets.
- Users face **slower performance** and occasional UI bugs when handling large datasets in Altair AI Studio.
- Users face **slow performance** with Altair AI Studio, particularly when managing large or complex datasets.
- Users find the **complexity issues** of Altair AI Studio frustrating, especially due to limited support options.
- Users find the **complex usage** of Altair AI Studio challenging due to steep learning curves and inadequate documentation.

#### What Are Recent G2 Reviews of Altair AI Studio?

**"[Essential Tool for Streamlined Sensor Analysis](https://www.g2.com/survey_responses/altair-ai-studio-review-12568188)"**

**Rating:** 5.0/5.0 stars
*— Ayçe M.*

[Read full review](https://www.g2.com/survey_responses/altair-ai-studio-review-12568188)

---

**"[Great tool for easy data analysis and testing of AI models](https://www.g2.com/survey_responses/altair-ai-studio-review-12942088)"**

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

[Read full review](https://www.g2.com/survey_responses/altair-ai-studio-review-12942088)

---


#### What Are G2 Users Discussing About Altair AI Studio?

- [What is RapidMiner used for?](https://www.g2.com/discussions/what-is-rapidminer-used-for) - 1 comment
- [What are the data mining tools?](https://www.g2.com/discussions/what-are-the-data-mining-tools)
- [Is RapidMiner open source?](https://www.g2.com/discussions/is-rapidminer-open-source)
- [How do you use the Rapid Miner?](https://www.g2.com/discussions/how-do-you-use-the-rapid-miner)
- [Is Rapid Miner legit?](https://www.g2.com/discussions/is-rapid-miner-legit)

### 8. [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:** 133

**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** of IBM watsonx.ai, enjoying its intuitive setup and seamless integration.
- Users value the **variety of model training options** offered by IBM watsonx.ai, enhancing customization and integration with workflows.
- Users value the **seamless integration of enterprise-grade AI** in IBM watsonx.ai, enhancing decision-making and operational efficiency.
- Users appreciate the **enterprise-grade AI integration** of IBM watsonx.ai, enhancing their ability to train models effectively.
- Users value the **enterprise-grade AI capabilities** of IBM watsonx.ai, enhancing reliability and scalability in real-world applications.

**Cons:**

- Users find the **difficult learning** curve of IBM watsonx.ai challenging, especially for those without technical expertise.
- Users find the **complexity of setup** in IBM watsonx.ai challenging, especially for small teams and beginners.
- Users find the **steep learning curve** of IBM watsonx.ai challenging, particularly for those without technical expertise.
- Users find the product **expensive** and complex, making it less suitable for small teams and beginners.
- Users find the **complex setup** of IBM watsonx.ai challenging, particularly 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)

---



### 9. [Clarifai](https://www.g2.com/products/clarifai/reviews)
Clarifai is a leader in AI orchestration and development, helping organizations, teams, and developers build, deploy, orchestrate, and operationalize AI at scale. Clarifai’s cutting-edge AI workflow orchestration platform leverages today's modern AI technologies like Large Language Models (LLMs), Large Vision Models (LVMs), and Retrieval Augmented Generation (RAG), data labeling, inference, and more, and is available in cloud, on-premises, or hybrid environments. Founded in 2013, Clarifai has been used to build more than 1.5 million AI models with more than 400,000 users in 170 countries. Learn more at www.clarifai.com.


**Average Rating:** 4.3/5.0
**Total Reviews:** 67

**Who Is the Company Behind Clarifai?**

- **Seller:** [Clarifai](https://www.g2.com/sellers/clarifai)
- **Year Founded:** 2013
- **HQ Location:** Wilmington, Delaware
- **Twitter:** @clarifai (10,922 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10064814/ (51 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 61% Small-Business, 27% Mid-Market


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

**Pros:**

- Features (13 reviews)
- AI Technology (10 reviews)
- Model Variety (10 reviews)
- AI Integration (8 reviews)
- AI Modeling (8 reviews)

**Cons:**

- Expensive (9 reviews)
- Complexity (4 reviews)
- Difficult Learning (3 reviews)
- Lack of Resources (3 reviews)
- Poor Documentation (3 reviews)


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

**Pros:**

- Users appreciate the **ease of use and powerful features** of Clarifai, making AI integration seamless for beginners.
- Users appreciate the **advanced AI capabilities** of Clarifai, recognizing its efficiency and accuracy in image and video recognition.
- Users find the **model variety** of Clarifai exceptional, benefiting from both prebuilt and customizable options for diverse applications.
- Users appreciate the **seamless AI integration** of Clarifai, enhancing image and video recognition with impressive accuracy.
- Users admire the **accuracy and efficiency** of Clarifai's advanced AI models for various identification tasks.

**Cons:**

- Users find the **pricing structure prohibitively expensive** for small projects, making it challenging for developers with limited resources.
- Users find the **complexity of setup** challenging, especially with advanced features and unclear documentation.
- Users find the **initial learning curve challenging** , particularly those unfamiliar with machine learning platforms and concepts.
- Users express concern over the **lack of resources** impacting affordability and support for small-scale developers and students.
- Users find the **poor documentation** to be a challenge, often needing to seek outside help for clarity.

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

**"[Clean UI, Powerful AI Platform with Reliable Performance and Responsive Support](https://www.g2.com/survey_responses/clarifai-review-13072233)"**

**Rating:** 5.0/5.0 stars
*— Ross M.*

[Read full review](https://www.g2.com/survey_responses/clarifai-review-13072233)

---

**"[Easy-to-Use AI Tools with Fast, Accurate Image & Video Recognition](https://www.g2.com/survey_responses/clarifai-review-12237261)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Computer Software*

[Read full review](https://www.g2.com/survey_responses/clarifai-review-12237261)

---



### 10. [Amazon SageMaker](https://www.g2.com/products/amazon-sagemaker/reviews)
Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management.


**Average Rating:** 4.3/5.0
**Total Reviews:** 53

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

- **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, Computer Software
- **Company Size:** 34% Mid-Market, 32% Enterprise


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

**Pros:**

- Ease of Use (3 reviews)
- AI Integration (2 reviews)
- Computing Power (2 reviews)
- Efficiency (2 reviews)
- Fast Processing (2 reviews)

**Cons:**

- Expensive (3 reviews)
- Complexity (2 reviews)
- Complexity Issues (2 reviews)
- Learning Curve (2 reviews)
- Difficult Learning (1 reviews)


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

**Pros:**

- Users find Amazon SageMaker’s **ease of use** exceptional, allowing quick adaptation and efficient model training experiences.
- Users value the **seamless AI integration** of Amazon SageMaker, enhancing the entire machine learning lifecycle efficiently.
- Users appreciate the **exceptional computing power** of Amazon SageMaker, significantly reducing model training time and enhancing their experience.
- Users appreciate the **efficiency** of Amazon SageMaker, significantly reducing model training time and simplifying experimentation.
- Users admire the **fast processing** of Amazon SageMaker, significantly reducing model training time compared to local setups.

**Cons:**

- Users find Amazon SageMaker **expensive** , particularly for long-term use and complex pricing, leading to unexpected costs.
- Users find Amazon SageMaker's **complex pricing** and steep learning curve challenging, often leading to unexpected expenses.
- Users find **complexity issues** in SageMaker's pricing and steep learning curve challenging for efficient usage.
- Users note a **steep learning curve** with Amazon SageMaker, making initial setup challenging for newcomers to AWS.
- Users face a **difficult learning curve** with the initial setup of Amazon SageMaker, complicating the onboarding process.

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

**"[A powerhouse for end-to-end ML, but be prepared for a steep learning curve](https://www.g2.com/survey_responses/amazon-sagemaker-review-12959870)"**

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

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-12959870)

---

**"[Fully Managed End-to-End ML in AWS with Powerful Distributed Training](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074)"**

**Rating:** 4.0/5.0 stars
*— Hem J.*

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074)

---


#### What Are G2 Users Discussing About Amazon SageMaker?

- [What is Amazon SageMaker used for?](https://www.g2.com/discussions/what-is-amazon-sagemaker-used-for)
- [Is AWS SageMaker good?](https://www.g2.com/discussions/is-aws-sagemaker-good) - 1 upvote
- [Who uses SageMaker?](https://www.g2.com/discussions/who-uses-sagemaker)
- [How do you use Amazon SageMaker?](https://www.g2.com/discussions/how-do-you-use-amazon-sagemaker)
- [What does Amazon SageMaker do?](https://www.g2.com/discussions/what-does-amazon-sagemaker-do)

### 11. [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

**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 highlight the **ease of use** of Pecan, enabling quick model building without needing deep technical expertise.
- Users commend Pecan’s **excellent customer support** , noting prompt assistance and valuable guidance throughout their experience.
- Users highlight the **speed of development** with Pecan, transforming model creation from months to weeks effortlessly.
- Users commend Pecan for its **effective problem-solving capabilities** , streamlining data to insights with excellent support.
- Users highlight the **implementation ease** of Pecan, enabling quick development and deployment of predictive models.

**Cons:**

- Users find a **learning difficulty** with Pecan, especially in understanding data structures and SQL requirements.
- Users desire **greater control over model selection** , wishing for customization options and specific optimization metrics.
- Users feel a lack of **customization options** , wishing for deeper control over model selection and optimization metrics.
- Users experience a **steep learning curve** initially, requiring time to understand data structure and SQL for effective use.
- Users desire more **customization options** for models and optimization metrics, limiting flexibility for specific use cases.

#### 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)

---



### 12. [Azure Machine Learning](https://www.g2.com/products/microsoft-azure-machine-learning/reviews)
Azure Machine Learning is an enterprise-grade service that facilitates the end-to-end machine learning lifecycle, enabling data scientists and developers to build, train, and deploy models efficiently. Key Features and Functionality: - Data Preparation: Quickly iterate data preparation on Apache Spark clusters within Azure Machine Learning, interoperable with Microsoft Fabric. - Feature Store: Increase agility in shipping your models by making features discoverable and reusable across workspaces. - AI Infrastructure: Take advantage of purpose-built AI infrastructure uniquely designed to combine the latest GPUs and InfiniBand networking. - Automated Machine Learning: Rapidly create accurate machine learning models for tasks including classification, regression, vision, and natural language processing. - Responsible AI: Build responsible AI solutions with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. - Model Catalog: Discover, fine-tune, and deploy foundation models from Microsoft, OpenAI, Hugging Face, Meta, Cohere, and more using the model catalog. - Prompt Flow: Design, construct, evaluate, and deploy language model workflows with prompt flow. - Managed Endpoints: Operationalize model deployment and scoring, log metrics, and perform safe model rollouts. Primary Value and Solutions Provided: Azure Machine Learning accelerates time to value by streamlining prompt engineering and machine learning model workflows, facilitating faster model development with powerful AI infrastructure. It streamlines operations by enabling reproducible end-to-end pipelines and automating workflows with continuous integration and continuous delivery (CI/CD). The platform ensures confidence in development through unified data and AI governance with built-in security and compliance, allowing compute to run anywhere for hybrid machine learning. Additionally, it promotes responsible AI by providing visibility into models, evaluating language model workflows, and mitigating fairness, biases, and harm with built-in safety systems.


**Average Rating:** 4.3/5.0
**Total Reviews:** 87

**Who Is the Company Behind Azure Machine Learning?**

- **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?**
- **Who Uses This:** Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 40% Enterprise, 33% Small-Business


#### What Are Azure Machine Learning's Pros and Cons?

**Pros:**

- Ease of Use (3 reviews)
- Features (3 reviews)
- Customer Support (2 reviews)
- Data Management (2 reviews)
- Efficiency (2 reviews)

**Cons:**

- Learning Curve (3 reviews)
- Difficult Navigation (2 reviews)
- UX Improvement (2 reviews)
- Complex Interface (1 reviews)
- Difficult Learning (1 reviews)


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

**Pros:**

- Users find Azure Machine Learning to be **easy to use** , with a user-friendly interface for managing data and experiments.
- Users value the **scalability and integration** of Azure Machine Learning, enhancing their AI deployment and management experience.
- Users praise the **excellent customer support** for Azure Machine Learning, highlighting helpful documentation and community resources.
- Users value the **ease of data management** in Azure Machine Learning, enhancing their ability to organize and analyze data efficiently.
- Users value the **efficiency** of Azure Machine Learning for launching and monitoring machine learning jobs easily.

**Cons:**

- Users face a significant **learning curve** with Azure Machine Learning, making initial use challenging and time-consuming.
- Users find the **difficult navigation** in Azure Machine Learning frustrating, often resulting in excessive clicks to locate options.
- Users find the **user interface disordered** , leading to a complicated experience with many required clicks.
- Users find the **complex interface** of Azure Machine Learning challenging, with unintuitive flows affecting usability.
- Users face a **difficult learning curve** with Azure Machine Learning, especially if they are new to the platform.

#### What Are Recent G2 Reviews of Azure Machine Learning?

**"[An Enterprise-Grade Way to Operationalize ML](https://www.g2.com/survey_responses/azure-machine-learning-review-12853548)"**

**Rating:** 4.0/5.0 stars
*— Vytas J.*

[Read full review](https://www.g2.com/survey_responses/azure-machine-learning-review-12853548)

---

**"[Cost-Efficient Medical Data Integration Backed by Great Support](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990)"**

**Rating:** 5.0/5.0 stars
*— Giridharan U.*

[Read full review](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990)

---


#### What Are G2 Users Discussing About Azure Machine Learning?

- [What is Azure Machine Learning Studio used for?](https://www.g2.com/discussions/what-is-azure-machine-learning-studio-used-for) - 1 comment
- [What type of data analysis is azure machine learning studio intended for?](https://www.g2.com/discussions/what-type-of-data-analysis-is-azure-machine-learning-studio-intended-for)
- [What are the key features of Azure Machine Learning?](https://www.g2.com/discussions/what-are-the-key-features-of-azure-machine-learning)
- [How do I use Microsoft Azure for machine learning?](https://www.g2.com/discussions/how-do-i-use-microsoft-azure-for-machine-learning)
- [What is Azure Machine Learning Studio?](https://www.g2.com/discussions/what-is-azure-machine-learning-studio)

### 13. [DataRobot](https://www.g2.com/products/datarobot/reviews)
DataRobot’s enterprise AI platform democratizes data science with end-to-end automation for building, deploying, and managing machine learning models. This platform maximizes business value by delivering AI at scale and continuously optimizing performance over time. The company’s proven combination of cutting edge software and world-class AI implementation, training, and support services, empowers any organization – regardless of size, industry, or resources – to drive better business outcomes with AI.


**Average Rating:** 4.4/5.0
**Total Reviews:** 27

**Who Is the Company Behind DataRobot?**

- **Seller:** [DataRobot](https://www.g2.com/sellers/datarobot)
- **Year Founded:** 2012
- **HQ Location:** Boston, Massachusetts
- **Twitter:** @DataRobot (19,225 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2672915/ (883 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software
- **Company Size:** 54% Small-Business, 29% Enterprise



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

**"[Incredible tool for quick raw data to insights](https://www.g2.com/survey_responses/datarobot-review-12876763)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Computer Software*

[Read full review](https://www.g2.com/survey_responses/datarobot-review-12876763)

---

**"[Fast, Insightful Automated Modeling with DataRobot](https://www.g2.com/survey_responses/datarobot-review-11788506)"**

**Rating:** 4.5/5.0 stars
*— Verified User in Financial Services*

[Read full review](https://www.g2.com/survey_responses/datarobot-review-11788506)

---



### 14. [Neuton AutoML](https://www.g2.com/products/neuton-automl/reviews)
Neuton (https://neuton.ai), a new AutoML solution, allows users to build compact AI models with just a few clicks and without any coding. Neuton also happens to be the most EXPLAINABLE Neural Network Framework and AutoML solution currently available on the market. It allows users to evaluate the model quality from various perspectives and interpret prediction results. Neuton Explainability Office: - Exploratory Data Analysis - Feature Importance Matrix with class granularity - Model Interpreter - Feature Influence Matrix - Validate Model on New Data - Model-to-Data Relevance Indicators historical and for every prediction - Model Quality Index - Confidence Interval - Extensive list of supported metrics with Radar Diagram


**Average Rating:** 4.5/5.0
**Total Reviews:** 17

**Who Is the Company Behind Neuton AutoML?**

- **Seller:** [Bell Integrator](https://www.g2.com/sellers/bell-integrator)
- **Year Founded:** 2003
- **HQ Location:** San Jose, CA
- **LinkedIn® Page:** https://www.linkedin.com/company/bellintegrator/ (703 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 35% Enterprise, 35% Small-Business



#### What Are Recent G2 Reviews of Neuton AutoML?

**"[A Comprehensive and Efficient Solution for Automating Machine Learning Model Development](https://www.g2.com/survey_responses/neuton-automl-review-7623010)"**

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

[Read full review](https://www.g2.com/survey_responses/neuton-automl-review-7623010)

---

**"[Cloud based ML platform for everyone.](https://www.g2.com/survey_responses/neuton-automl-review-8043519)"**

**Rating:** 4.5/5.0 stars
*— Abhuday T.*

[Read full review](https://www.g2.com/survey_responses/neuton-automl-review-8043519)

---


#### What Are G2 Users Discussing About Neuton AutoML?

- [What is Neuton AutoML used for?](https://www.g2.com/discussions/what-is-neuton-automl-used-for)

### 15. [Google Cloud AutoML](https://www.g2.com/products/google-cloud-automl/reviews)
Google Cloud AutoML is a suite of machine learning products designed to enable developers with limited expertise to train high-quality custom models tailored to their specific business needs. By leveraging Google's advanced transfer learning and neural architecture search technologies, AutoML simplifies the process of building, deploying, and scaling machine learning models, making AI more accessible to a broader audience. Key Features and Functionality: - Automated Model Training: AutoML automates the selection of model architecture and hyperparameter tuning, reducing the need for manual intervention and specialized knowledge. - User-Friendly Interface: The platform offers an intuitive graphical interface that allows users to upload data, train models, and manage deployments with ease. - Versatile Model Types: AutoML supports various data types and tasks through specialized services: - AutoML Vision: For image classification and object detection. - AutoML Natural Language: For text classification, sentiment analysis, and entity recognition. - AutoML Translation: For creating custom translation models between language pairs. - AutoML Video Intelligence: For video classification and object tracking. - AutoML Tables: For structured data tasks like regression and classification. - Seamless Integration: AutoML integrates with other Google Cloud services, facilitating efficient data management, model deployment, and scalability. Primary Value and Problem Solving: Google Cloud AutoML democratizes machine learning by enabling users without deep technical expertise to develop and deploy custom models. This accessibility allows businesses to harness the power of AI to solve complex problems, such as improving customer experiences through personalized recommendations, automating content moderation, enhancing language translation services, and gaining insights from large datasets. By reducing the barriers to entry, AutoML empowers organizations to innovate and stay competitive in their respective industries.


**Average Rating:** 4.1/5.0
**Total Reviews:** 23

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

- **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, 29% Mid-Market


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

**Pros:**

- AI Integration (1 reviews)
- Ease of Use (1 reviews)
- Easy Integrations (1 reviews)
- Integrated Platform (1 reviews)
- Intuitive (1 reviews)

**Cons:**

- Cost (1 reviews)
- Expensive (1 reviews)


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

**Pros:**

- Users appreciate the **seamless AI integration** of Google Cloud AutoML, simplifying high-quality model training for everyone.
- Users find Google Cloud AutoML's **intuitive interface** and seamless integration greatly enhances their machine learning experience.
- Users value the **easy integrations** of Google Cloud AutoML, enjoying its seamless connection with other Google Cloud services.
- Users value the **seamless integration** of Google Cloud AutoML with other Google Cloud services, enhancing their overall experience.
- Users value the **intuitive interface** of Google Cloud AutoML, facilitating easier machine learning model training for all skill levels.

**Cons:**

- Users find the **cost prohibitive** for small projects or students, impacting accessibility and budget considerations.
- Users find the **pricing expensive** , making it challenging for small projects and students to utilize Google Cloud AutoML.

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

**"[Easy to Use Yet Customizable, with Room to Grow into Vertex AI](https://www.g2.com/survey_responses/google-cloud-automl-review-13076121)"**

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

[Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-13076121)

---

**"[Faster ML Model Development by Google](https://www.g2.com/survey_responses/google-cloud-automl-review-13088393)"**

**Rating:** 4.0/5.0 stars
*— Verified User in Information Technology and Services*

[Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-13088393)

---


#### What Are G2 Users Discussing About Google Cloud AutoML?

- [What is Google Cloud AutoML used for?](https://www.g2.com/discussions/what-is-google-cloud-automl-used-for)

### 16. [H2O Driverless AI](https://www.g2.com/products/h2o-driverless-ai/reviews)
H2O Driverless AI employs the techniques of expert data scientists in an easy to use application that helps scale your data science efforts. Driverless AI empowers data scientists to work on projects faster using automation and state-of-the-art computing power from GPUs to accomplish tasks in minutes that used to take months. With Driverless AI, everyone including expert and junior data scientists, domain scientists, and data engineers can develop trusted machine learning models. This next-generation automatic machine learning platform delivers unique and advanced functionality for data visualization, feature engineering, model interpretability and low-latency deployment.


**Average Rating:** 4.3/5.0
**Total Reviews:** 4

**Who Is the Company Behind H2O Driverless AI?**

- **Seller:** [H2O.ai](https://www.g2.com/sellers/h2o-ai)
- **Year Founded:** 2012
- **HQ Location:** Mountain View, CA
- **Twitter:** @h2oai (25,222 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2820918/ (345 employees on LinkedIn®)

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


#### What Are H2O Driverless AI's Pros and Cons?

**Pros:**

- Ease of Use (2 reviews)
- Coding Ease (1 reviews)
- Machine Learning (1 reviews)
- Problem Solving (1 reviews)

**Cons:**

- Inadequate Tools (1 reviews)
- Limited Features (1 reviews)
- UX Improvement (1 reviews)


### What Do G2 Reviewers Say About H2O Driverless AI?
*AI-generated summary from verified user reviews*

**Pros:**

- Users highlight the **ease of use** of H2O Driverless AI, achieving great results effortlessly.
- Users appreciate the **easy-to-use, low-code programming** of H2O Driverless AI, enhancing their experience in Data Science.
- Users value the **easy-to-use AutoML platform** of H2O Driverless AI, making data science accessible with low-code programming.
- Users value the **effective problem-solving capabilities** of H2O Driverless AI, appreciating its ease of use and results.

**Cons:**

- Users find the lack of **data preparation tools** in H2O Driverless AI limiting for end-user functionality.
- Users feel that Driverless AI lacks **data preparation tools** , limiting its effectiveness for end-users and data engineering.
- Users struggle with the **user interface and lack of ETL functionalities** , which hampers their overall experience with H2O Driverless AI.

#### What Are Recent G2 Reviews of H2O Driverless AI?

**"[Excellent tool for fast model production](https://www.g2.com/survey_responses/h2o-driverless-ai-review-10983629)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Information Technology and Services*

[Read full review](https://www.g2.com/survey_responses/h2o-driverless-ai-review-10983629)

---

**"[H2O Driverless AI Review](https://www.g2.com/survey_responses/h2o-driverless-ai-review-4358250)"**

**Rating:** 4.5/5.0 stars
*— Nguyen D.*

[Read full review](https://www.g2.com/survey_responses/h2o-driverless-ai-review-4358250)

---


#### What Are G2 Users Discussing About H2O Driverless AI?

- [Is H2O driverless AI free?](https://www.g2.com/discussions/is-h2o-driverless-ai-free) - 1 comment
- [What is H20 driverless AI?](https://www.g2.com/discussions/what-is-h20-driverless-ai) - 1 comment
- [What is H2O AI used for?](https://www.g2.com/discussions/what-is-h2o-ai-used-for) - 1 comment

### 17. [BigML](https://www.g2.com/products/bigml/reviews)
Enjoy the power of Programmatic Machine Learning


**Average Rating:** 4.7/5.0
**Total Reviews:** 24

**Who Is the Company Behind BigML?**

- **Seller:** [BigML](https://www.g2.com/sellers/bigml)
- **Year Founded:** 2011
- **HQ Location:** Corvallis, OR
- **Twitter:** @bigmlcom (6,077 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1742510 (30 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Software Engineer, Senior Software Engineer
- **Top Industries:** Computer Software
- **Company Size:** 88% Small-Business, 8% Mid-Market



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

**"[Machine Learning Platform with cloud based for Data processing](https://www.g2.com/survey_responses/bigml-review-8031197)"**

**Rating:** 4.5/5.0 stars
*— Nitin Y.*

[Read full review](https://www.g2.com/survey_responses/bigml-review-8031197)

---

**"[My Experience about BigML](https://www.g2.com/survey_responses/bigml-review-8076641)"**

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

[Read full review](https://www.g2.com/survey_responses/bigml-review-8076641)

---



### 18. [dotData Enterprise](https://www.g2.com/products/dotdata-enterprise/reviews)
dotData Pioneered the AutoML 2.0 full-cycle data science automation platform. Fortune 500 organizations around the world use dotData to accelerate their ML and AI projects and deliver higher business value. dotData’s automated data science platform speeds time to value by accelerating, democratizing, augmenting and operationalizing the entire data science process, from raw business data through data and feature engineering to machine learning in production. With solutions designed to cater to the needs of both data scientists as well as citizen data scientists, dotData provides unmatched value across the entire organization. dotData’s unique AI-powered feature engineering delivers actionable business insights from relational, transactional, temporal, geo-locational, and text data. dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the “best machine learning platform” for 2019 by the AI breakthrough awards and was named an “emerging vendor to watch” by CRN in the big data space. For more information, visit www.dotdata.com, and join the conversation on Twitter and LinkedIn.



**Who Is the Company Behind dotData Enterprise?**

- **Seller:** [dotData](https://www.g2.com/sellers/dotdata)
- **Year Founded:** 2018
- **HQ Location:** San Mateo, US
- **Twitter:** @dotDataUS (268 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dotdatainc (94 employees on LinkedIn®)






### 19. [Neuton AutoML](https://www.g2.com/products/b2saas-neuton-automl/reviews)
Neuton AutoML is an innovative no-code platform that empowers users to build, train, and deploy highly compact and efficient machine learning models without requiring extensive programming knowledge. Leveraging a patented neural network framework, Neuton automates the entire machine learning lifecycle, enabling the creation of models optimized for deployment on resource-constrained devices such as microcontrollers (MCUs) and sensors. This approach democratizes AI development, making it accessible to businesses and developers aiming to implement machine learning solutions swiftly and effectively. Key Features and Functionality: - No-Code Model Building: Users can generate AI models through an intuitive interface, eliminating the need for specialized programming skills. - Compact and Efficient Models: Neuton produces neural network models that are significantly smaller than those generated by traditional frameworks, often achieving model sizes measured in kilobytes rather than megabytes. - Automated Workflow: The platform provides a fully automated pipeline from data upload to model deployment without requiring programming knowledge or manual hyperparameter tuning. - Fast Training on Standard Hardware: Neuton's training algorithms are optimized to run efficiently on standard CPU infrastructure without requiring expensive GPU resources. Primary Value and User Solutions: Neuton AutoML addresses the challenges of deploying machine learning models in environments with limited computational resources by generating ultra-compact models suitable for edge devices. Its no-code approach lowers the barrier to entry for AI development, enabling businesses to implement machine learning solutions without the need for extensive data science expertise. By automating the model development process and producing efficient models, Neuton facilitates faster deployment, reduces development costs, and supports real-time inference on devices with constrained resources.



**Who Is the Company Behind Neuton AutoML?**

- **Seller:** [B2Saas](https://www.g2.com/sellers/b2saas)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/b2saas (1 employees on LinkedIn®)






### 20. [Spotfire Enterprise](https://www.g2.com/products/spotfire-spotfire-enterprise/reviews)
Spotfire® is a visual data science platform designed to help organizations address complex, industry-specific challenges by effectively utilizing data. This solution offers a range of flexible packaging options tailored to meet the diverse needs of teams at various stages of their visual data science journey. Spotfire® Data Science is built to help organizations solve complex, mission-critical challenges with scalable, visual data science. It combines the power of machine learning, statistical modeling, and process optimization in an intuitive, collaborative environment, enabling both data scientists and domain experts to deliver insights with speed, precision, and confidence. Designed with industry in mind, it equips teams with specialized data functions, visualizations, and mods tailored to the needs of energy, manufacturing, and other data-intensive sectors. Building on the capabilities of Spotfire Analytics, Spotfire Data Science takes analysis a step further by offering advanced tools for data understanding and preparation. Users can profile data, detect outliers, handle missing values, analyze correlations, and preprocess time series data to uncover meaningful patterns and insights. Predictive modeling is enhanced with built-in machine learning and statistical algorithms, complete with explainability features and dimensionality reduction techniques to better interpret complex results. Spotfire Data Science also provides a comprehensive suite of process improvement tools, ranging from design of experiments to reliability analysis and statistical process control, enabling organizations to continuously optimize operations and enhance product quality. Deep integrations with R, Python, and Jupyter notebooks provide experts with the flexibility to extend and customize their analyses. At the same time, native connectivity to industry-specific data sources ensures seamless integration with existing workflows. Whether the goal is to predict outcomes, optimize processes, or solve industry-specific problems, Spotfire Data Science empowers teams to turn raw data into strategic insight. Uniting advanced analytics with visual exploration enables organizations to address their toughest challenges and achieve measurable business impact. Scale from advanced visual analytics to industrial analytics - combining Spotfire’s interactive experience with domain-specific statistical depth. Spotfire Data Science extends Spotfire Analytics with domain-specific visualizations, workflows, and algorithms built for industrial analytics in energy, manufacturing, and life science. Profile and visually clean data, detect outliers, handle missing values, and preprocess time series. Then apply predictive models, machine learning, and statistical process control—all in an intuitive, collaborative, visual environment. Use built-in algorithms and use process optimization tools to accelerate discovery. From quality improvement to predictive maintenance, Spotfire Data Science helps engineers, scientists, and domain experts turn complex data into confident, measurable outcomes. Best for: Engineers, scientists, domain-experts, and industrial analytics teams tackling mission-critical challenges.



**Who Is the Company Behind Spotfire Enterprise?**

- **Seller:** [Spotfire](https://www.g2.com/sellers/spotfire-2d87c926-94f3-47ce-8a5d-44d930d7c744)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/spotfire/ (103 employees on LinkedIn®)






### 21. [Xyzt](https://www.g2.com/products/xyzt/reviews)
xyzt.ai is a high-performance, no-code location intelligence platform designed for scale. The enterprise-grade SaaS platform enables organizations to instantly visualize and analyze billions of spatio-temporal IoT and tracking records in seconds, without requiring custom data engineering or data science teams. Purpose-built for large-scale geospatial, movement, and time-series data, xyzt.ai natively supports all major data formats, including raw GPS traces, map-matched trip paths, and aggregated traffic volumes. With rapid cloud deployment completed in less than a day, users can seamlessly integrate and cross-analyze heterogeneous datasets from connected vehicles, assets, infrastructure, and environmental sensors.xyzt.ai is trusted globally across key industries to turn big data into real-time situational awareness: - Maritime & Ports: Analyze historical and live AIS data to optimize vessel traffic, improve maritime safety, and track port efficiency. - Smart Cities & Urban Mobility: Visualize floating vehicle data (FVD), floating cellular data, and traffic flows to eliminate congestion and support infrastructure planning. - Logistics & Asset Tracking: Monitor supply chain performance, evaluate asset utilization, and improve operational safety. - Sustainability: Drive green initiatives through emissions tracking, resource optimization, and environmental impact evaluation. What differentiates xyzt.ai is its unique blend of massive scale, total data privacy by design, and extreme usability. Domain experts can interactively explore billions of records, detect AI-driven anomalies, and unlock hidden trends instantly through a seamless, self-service visual analytics experience.



**Who Is the Company Behind Xyzt?**

- **Seller:** [xyzt.ai](https://www.g2.com/sellers/xyzt-ai)
- **Year Founded:** 2020
- **HQ Location:** Leuven, BE
- **LinkedIn® Page:** https://www.linkedin.com/company/xyzt-ai/ (7 employees on LinkedIn®)







## What Is  Low-Code Machine Learning Platforms Software?

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

## What Software Categories Are Similar to  Low-Code Machine Learning Platforms Software?

- [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics)
- [Analytics Platforms](https://www.g2.com/categories/analytics-platforms)
- [Machine Learning Software](https://www.g2.com/categories/machine-learning)
- [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)
- [Generative AI Infrastructure Software](https://www.g2.com/categories/generative-ai-infrastructure)
- [Large Language Model Operationalization (LLMOps) Software](https://www.g2.com/categories/large-language-model-operationalization-llmops)


---
## What Are the Most Common Questions About  Low-Code Machine Learning Platforms Software?
*AI-generated · Last updated: June  3, 2026*
### Which low code ml platform gives the best balance of price and automation features
Based on G2 reviews, these products are the most consistently mentioned for low-code automation and model-building workflows.

- [Alteryx](https://www.g2.com/products/alteryx) — automated data prep and reporting.
- [SAS Viya](https://www.g2.com/products/sas-sas-viya) — automated ML pipelines and dashboards.
- [Dataiku](https://www.g2.com/products/dataiku) — visual workflows with coding flexibility.
- [Altair AI Studio](https://www.g2.com/products/rapidminer-studio) — AutoML with visual workflow design.


### Low code machine learning platforms that integrate well with existing web apps and APIs
According to verified users, integration strength shows up in a few recurring patterns: API access, compatibility with existing cloud or business systems, and the ability to move data between tools without heavy custom work. Recent reviewers highlight platforms that connect to multiple data sources, support open languages or APIs, and fit into broader enterprise workflows. They also note that integration quality can vary by connector and environment, especially when teams need external systems, custom pipelines, or cross-cloud support. Buyers evaluating this area should look closely at how easily a platform handles ingestion, deployment, and workflow handoffs using the systems their team already depends on.


### What���s the easiest low code ml platform for a non data scientist to build models
According to verified users, ease for non-data scientists usually comes from drag-and-drop design, guided workflows, and the option to build models without writing code. Recent G2 reviews frequently mention beginner-friendly interfaces, visual pipelines, AutoML support, and smoother onboarding when platforms balance simplicity with room to grow. Reviewers also point out that many products are approachable at first but still have learning curves once projects become more complex or datasets get larger. For buyers, the most practical signal is whether non-technical users can prepare data, test models, and share outputs without relying on specialists for every step of the workflow.


### What are the best low code machine learning platforms
Based on G2 reviews, these products appear most often in recent feedback for low-code machine learning use cases.

- [Alteryx](https://www.g2.com/products/alteryx) — drag-and-drop data prep and automation.
- [SAS Viya](https://www.g2.com/products/sas-sas-viya) — visual modeling and governed analytics.
- [Dataiku](https://www.g2.com/products/dataiku) — collaborative end-to-end ML workflows.
- [Altair AI Studio](https://www.g2.com/products/rapidminer-studio) — no-code modeling for engineering data.


### What features define modern low-code machine learning platforms
According to verified users, modern low-code machine learning platforms are defined by visual workflow building, automated model selection, data preparation tools, and the flexibility to mix no-code steps with code when needed. Recent reviews also repeatedly mention dashboarding, reporting, deployment support, model monitoring, collaboration, and integration with common data sources or APIs. Buyers should also weigh practical usability signals that come up often in reviews, such as onboarding experience, interface clarity, workflow speed, and how well the platform supports both technical and non-technical contributors. The strongest products tend to reduce manual work while keeping model building, data movement, and operational handoffs in one environment.