# 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.





## Category Overview

**Total Products under this Category:** 20


## Trust & Credibility Stats

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

- 30 Analysts and Data Experts
- 3,400+ Authentic Reviews
- 20+ 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.


## Best  Low-Code Machine Learning Platforms Software At A Glance

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



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---

## Top-Rated Products (Ranked by G2 Score)
### 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:** 751


**Seller Details:**

- **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 (61,004 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,519 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Student, Statistical Programmer
  - **Top Industries:** Pharmaceuticals, Banking
  - **Company Size:** 33% Enterprise, 33% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (316 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)

### 2. [Dataiku](https://www.g2.com/products/dataiku/reviews)
  Dataiku is the Platform for AI Success that unites people, orchestration, and governance to turn AI investments into measurable business outcomes. It helps organizations move from fragmented experimentation to coordinated, trusted execution at scale. Built for AI success: Dataiku brings business experts and AI specialists into the same environment, embedding business context into analytics, models, and AI agents. Business teams can self-serve and innovate, while AI experts build, deploy, and optimize quickly, closing the gap between pilots and production. Orchestration that scales: Dataiku connects data, AI services, and enterprise apps across analytics, machine learning, and AI agents. Integrated workflows deliver value across any cloud or infrastructure without vendor lock-in or fragmentation. Governance you can trust: Dataiku embeds governance across the AI lifecycle, enabling teams to track performance, cost, and risk to keep systems explainable, compliant, and auditable.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 185


**Seller Details:**

- **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku)
- **Company Website:** https://Dataiku.com
- **Year Founded:** 2013
- **HQ Location:** New York, NY
- **Twitter:** @dataiku (22,942 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dataiku/ (1,609 employees on LinkedIn®)

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


#### Pros & 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)

### 3. [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:** 646


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,910,461 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

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


#### Pros & Cons

**Pros:**

- Ease of Use (162 reviews)
- Model Variety (114 reviews)
- Features (109 reviews)
- Machine Learning (104 reviews)
- Easy Integrations (84 reviews)

**Cons:**

- Expensive (75 reviews)
- Learning Curve (63 reviews)
- Complexity (62 reviews)
- Complexity Issues (58 reviews)
- Difficult Learning (47 reviews)

### 4. [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:** 650


**Seller Details:**

- **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx)
- **Company Website:** https://www.alteryx.com
- **Year Founded:** 1997
- **HQ Location:** Irvine, CA
- **Twitter:** @alteryx (26,218 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,268 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Analyst
  - **Top Industries:** Financial Services, Accounting
  - **Company Size:** 62% Enterprise, 23% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (331 reviews)
- Automation (146 reviews)
- Intuitive (131 reviews)
- Easy Learning (102 reviews)
- Efficiency (102 reviews)

**Cons:**

- Expensive (86 reviews)
- Learning Curve (80 reviews)
- Missing Features (62 reviews)
- Learning Difficulty (55 reviews)
- Slow Performance (41 reviews)

### 5. [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


**Seller Details:**

- **Seller:** [Qlik](https://www.g2.com/sellers/qlik)
- **Year Founded:** 1993
- **HQ Location:** Radnor, PA
- **Twitter:** @qlik (64,263 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10162/ (4,529 employees on LinkedIn®)
- **Phone:** 1 (888) 994-9854

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


#### Pros & 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)

### 6. [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:** 490


**Seller Details:**

- **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/ (3,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:ALTR

**Reviewer Demographics:**
  - **Who Uses This:** Student, Data Scientist
  - **Top Industries:** Higher Education, Education Management
  - **Company Size:** 43% Small-Business, 30% Mid-Market


#### Pros & 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)

### 7. [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:** 4.4/5.0
  **Total Reviews:** 70


**Seller Details:**

- **Seller:** [KNIME](https://www.g2.com/sellers/knime)
- **Year Founded:** 2008
- **HQ Location:** Zurich, Switzerland
- **Twitter:** @knime (8,015 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 (245 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Higher Education
  - **Company Size:** 49% Enterprise, 35% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (8 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)

### 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:** 131


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Company Website:** https://www.ibm.com/us-en
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,390 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Consultant
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 41% Small-Business, 31% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (76 reviews)
- Model Variety (31 reviews)
- Features (29 reviews)
- AI Integration (28 reviews)
- AI Capabilities (23 reviews)

**Cons:**

- Difficult Learning (21 reviews)
- Complexity (20 reviews)
- Learning Curve (19 reviews)
- Expensive (17 reviews)
- Improvement Needed (16 reviews)

### 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:** 66


**Seller Details:**

- **Seller:** [Clarifai](https://www.g2.com/sellers/clarifai)
- **Year Founded:** 2013
- **HQ Location:** Wilmington, Delaware
- **Twitter:** @clarifai (10,767 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10064814/ (89 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 61% Small-Business, 27% Mid-Market


#### Pros & 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)

### 10. [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:** 35


**Seller Details:**

- **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 (1,140 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/pecan-ai/ (83 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Retail
  - **Company Size:** 53% Mid-Market, 21% Enterprise


#### Pros & 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)

### 11. [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:** 85


**Seller Details:**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,114,353 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®)
- **Ownership:** MSFT

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 39% Enterprise, 34% Small-Business


#### Pros & 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)

### 12. [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


**Seller Details:**

- **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/ (709 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 35% Enterprise, 35% Small-Business


### 13. [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:** 4.2/5.0
  **Total Reviews:** 45


**Seller Details:**

- **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 (2,225,864 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 33% Enterprise, 31% Mid-Market


#### Pros & 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)

### 14. [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


**Seller Details:**

- **Seller:** [H2O.ai](https://www.g2.com/sellers/h2o-ai)
- **Year Founded:** 2012
- **HQ Location:** Mountain View, CA
- **Twitter:** @h2oai (25,269 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2820918/ (335 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Small-Business, 25% Mid-Market


#### Pros & 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)

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


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


**Seller Details:**

- **Seller:** [BigML](https://www.g2.com/sellers/bigml)
- **Year Founded:** 2011
- **HQ Location:** Corvallis, OR
- **Twitter:** @bigmlcom (6,088 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1742510 (30 employees on LinkedIn®)

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


### 16. [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:** 25


**Seller Details:**

- **Seller:** [DataRobot](https://www.g2.com/sellers/datarobot)
- **Year Founded:** 2012
- **HQ Location:** Boston, Massachusetts
- **Twitter:** @DataRobot (19,274 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2672915/ (870 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 58% Small-Business, 31% Enterprise


### 17. [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.




**Seller Details:**

- **Seller:** [dotData](https://www.g2.com/sellers/dotdata)
- **Year Founded:** 2018
- **HQ Location:** San Mateo, US
- **Twitter:** @dotDataUS (273 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dotdatainc (94 employees on LinkedIn®)



### 18. [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:** 22


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,910,461 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Company Size:** 45% Small-Business, 27% Mid-Market


#### Pros & 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)

### 19. [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.




**Seller Details:**

- **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®)



### 20. [Xyzt](https://www.g2.com/products/xyzt/reviews)
  xyzt.ai is a leading no-code data analytics platform for location intelligence. It enables organizations to unlock insights from large-scale geospatial, movement, and time-series data, without the need for coding or complex data engineering. Bring your own data, analyze billions of data points in seconds, and work with all major data formats. As the volume and diversity of data continue to grow, from connected assets and sensors to infrastructure and environmental systems, many organizations struggle to extract value due to complexity. xyzt.ai removes these barriers by allowing users to seamlessly integrate, explore, and analyze heterogeneous datasets in one platform. With rapid deployment, typically in less than a day, users can start analyzing data immediately. The platform transforms billions of records into intuitive visual insights, enabling users to detect patterns, monitor operations, and support data-driven decision-making in real time. xyzt.ai supports a wide range of applications across industries. Organizations can analyze movement patterns, monitor asset performance, evaluate operational impact, and improve safety and efficiency. The platform also enables sustainability-focused use cases, such as emissions tracking and resource optimization. What differentiates xyzt.ai is its unique combination of flexibility, scale, and usability. Domain experts can work directly with their own data, regardless of source, format, or size, and interactively explore massive datasets without relying on data scientists or custom-built tools. Trusted by leading organizations worldwide, xyzt.ai empowers teams to turn complex data into actionable insights, driving smarter decisions, more efficient operations, and more sustainable outcomes.




**Seller Details:**

- **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®)





## Parent Category

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



## Related Categories

- [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics)
- [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)