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Microsoft Corporation

Microsoft Azure Machine Learning

Composite Score

8.9 /10

CX Score

9.1 /10

61 Reviews Write a Review

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What is Microsoft Azure Machine Learning?

Machine Learning Studio is a powerfully simple browser-based, visual drag-and-drop authoring environment where no coding is necessary. Go from idea to deployment in a matter of clicks. Microsoft Azure Machine Learning Studio is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data. Machine Learning Studio publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel.

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Awards & Recognition

Microsoft Azure Machine Learning won the following awards in the Machine Learning Platforms category

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2025 Emotional Footprint Champion

Latest Award, 2025

Product Impact

Latest Award, 2025

2025 Data Quadrant Champion

2025

6 Top Rated Capabilities

6 Top Rated Features

2025

2024 Emotional Footprint Champion

2024

Product Impact

2024

2024 Data Quadrant Champion

2024

5 Top Rated Capabilities

9 Top Rated Features

2024

2023 Emotional Footprint Champion

2023

Strategy and Innovation
Service Experience
Product Impact

2023

2023 Data Quadrant Champion

2023

6 Top Rated Capabilities

13 Top Rated Features

2023

Microsoft Azure Machine Learning Ratings

Real user data aggregated to summarize the product performance and customer experience.
Download the entire Product Scorecard to access more information on Microsoft Azure Machine Learning.

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

90 Likeliness to Recommend

100 Plan to Renew

85 Satisfaction of Cost Relative to Value

1
Since last award

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{name}

Emotional Footprint Overview

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

+94 Net Emotional Footprint

The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.

How much do users love Microsoft Azure Machine Learning?

2% Negative

2% Neutral

96% Positive

Pros

Performance Enhancing
Respectful
Enables Productivity
Reliable

Feature Ratings

Average 85

Data Exploration and Visualization

Data Pre-Processing

Model Training

Pre-Packaged AI/ML Services

Feature Engineering

Model Tuning

Model Monitoring and Management

Data Labeling

Algorithm Recommendation

Ensembling

Data Ingestion

Vendor Capability Ratings

Average 83

Ease of Data Integration

Ease of IT Administration

Quality of Features

Ease of Implementation

Ease of Customization

Product Strategy and Rate of Improvement

Breadth of Features

Usability and Intuitiveness

Availability and Quality of Training

Business Value Created

Vendor Support

Microsoft Azure Machine Learning Reviews

Ned E.

Role: Information Technology
Industry: Technology
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Sep 2025

Powerful and Scalable, But Complex for Beginners

Likeliness to Recommend

9 /10

What differentiates Microsoft Azure Machine Learning from other similar products?

Microsoft Azure Machine Learning stands out for its tight integration with the Azure ecosystem, offering end-to-end tools from data prep to deployment. Unlike some platforms, it scales easily for enterprise needs while supporting both no-code interfaces and advanced coding workflows.

What is your favorite aspect of this product?

My favorite aspect of Microsoft Azure Machine Learning is its flexibility—it supports both beginners using drag-and-drop tools and experts coding with Python or R. This makes it versatile for different skill levels within the same organization.

What do you dislike most about this product?

What I dislike most about Microsoft Azure Machine Learning is its complexity, especially for beginners, as the interface and setup can feel overwhelming. Managing costs can also be tricky without careful monitoring of resources.

What recommendations would you give to someone considering this product?

I’d recommend Microsoft Azure Machine Learning if you need a powerful, enterprise-ready platform that grows with your AI projects. Just be prepared for a learning curve and invest time in training or support to get the most value while keeping costs under control.

Pros

Continually Improving Product
Reliable
Efficient Service
Inspires Innovation

Opeyemi B.

Role: Sales Marketing
Industry: Education
Involvement: Business Leader or Manager

Validated Review

Verified Reviewer

Submitted Sep 2025

Why Businesses Are Moving to Microsoft Azure ML

Likeliness to Recommend

10 /10

What differentiates Microsoft Azure Machine Learning from other similar products?

Azure ML stands out for its Microsoft ecosystem integration, strong responsible AI features, hybrid deployment flexibility, and enterprise-grade compliance making it a strong choice for organizations already invested in Microsoft or needing robust governance and scalability.

What is your favorite aspect of this product?

My favorite aspect of Microsoft Azure Machine Learning is its seamless integration with the broader Microsoft ecosystem, making it easy to connect with tools like Power BI, Dynamics 365, and Azure services. I also value its strong governance, responsible AI features, and enterprise-grade security for regulated industries.

What do you dislike most about this product?

The platform can feel overwhelming for beginners due to its complex interface and the depth of features that require technical expertise. Additionally, costs can escalate quickly when scaling models or integrating multiple Azure services.

What recommendations would you give to someone considering this product?

If you’re considering Azure ML, make sure your organization is already invested in the Microsoft ecosystem to maximize value and reduce friction. Start small with the low-code/no-code tools, then scale into advanced features as your team gains experience.

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

Sienna W.

Role: Information Technology
Industry: Machinery
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Aug 2025

Fruitful Machine Learning App

Likeliness to Recommend

7 /10

What differentiates Microsoft Azure Machine Learning from other similar products?

We have managed all the learning demands through this powerful software The guidelines from this app are reputable and coherent

What is your favorite aspect of this product?

The intellectual part of this app helps us in pursuing business ideas The quality time management through automated speed is also a consideration

What do you dislike most about this product?

The objective nature of this application creates a unique opportunity

What recommendations would you give to someone considering this product?

The intellectual capacity of this application creates is remarkable

Pros

Reliable
Trustworthy
Helps Innovate
Continually Improving Product

Microsoft Azure Machine Learning

What is Microsoft Azure Machine Learning?

Company Details

Need Assistance?

Awards & Recognition

2025 Emotional Footprint Champion

2025 Data Quadrant Champion

2024 Emotional Footprint Champion

2024 Data Quadrant Champion

2023 Emotional Footprint Champion

2023 Data Quadrant Champion

Microsoft Azure Machine Learning Ratings

90 Likeliness to Recommend

100 Plan to Renew

85 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+94 Net Emotional Footprint

How much do users love Microsoft Azure Machine Learning?

Pros

Feature Ratings Average rating of all product features, some of which may not be shown below

Data Exploration and Visualization Ability to visually manipulate, interact with, and explore data, and perform basic descriptive statistics and pattern detection.

Data Pre-Processing Ability to wrangle or pre-process data to ready it for model training.

Model Training Ability to train a model with a range of data sets that you provide.

Pre-Packaged AI/ML Services Pre-trained models and associated resources, e.g., for forecasting, recommendations, image and video analysis, NLP, voice, etc.

Feature Engineering Ability to extract and build features from raw data.

Model Tuning Ability to fine-tune models based on monitoring feedback within the platform.

Model Monitoring and Management Ability to monitor model use and performance in production, incl. identifying models for refresh and/or replacement.

Data Labeling Ability to annotate/ label data to create a training data set.

Algorithm Recommendation Ability to recommend a best ML algorithm to use based on dataset characteristics.

Ensembling 'Ability to combine ML models for best results.

Data Ingestion Ability to access data from various sources on-prem and in the cloud and of various type: structured, unstructured, and semi-structured, real-time, etc.

Vendor Capability Ratings Average rating of all vendor capabilities, some of which may not be shown below

Ease of Data Integration Use this data to determine whether the product will cause headaches or make data integration easy.

Ease of IT Administration This data indicates whether IT personnel will be able to resolve issues and perform configurations efficiently and effectively.

Quality of Features Feature quality is just as important as quantity. Use this data to determine if this product will do what you're purchasing it to do, easily, intuitively, reliably, and effectively.

Ease of Implementation Successfully implementing new software is necessary to realize its full value and promote end user adoption. This data indicates whether or not the product is easy to implement.

Ease of Customization Don't get bogged down in a difficult customization; use this data to make sure you can easily achieve the functionality you need for your particular situation.

Product Strategy and Rate of Improvement Vendors who don't stay on top of emerging needs and trends won't enable you to meet your business goals. Use this data to separate innovators from imposters.

Breadth of Features Users prefer feature rich software that enables them to perform diverse series of tasks. This data expresses user satisfaction with the product's breadth of features.

Usability and Intuitiveness End user learning curves cost the organization money. Pay attention to your end users' technical ability to determine how important UX is in your purchase.

Availability and Quality of Training Effective and readily available training enables users to get the most out of the software you've chosen. Use this section to make sure your vendor's training programs and materials measure up.

Business Value Created Software needs to create value for employees, customers, partners, and, ultimately, shareholders. This data expresses user satisfaction - or lack thereof - with the product's business value.

Vendor Support The importance of vendor support will vary for each organization depending on internal capabilities, but there will always be issues that only the vendor can resolve.

Microsoft Azure Machine Learning Reviews

Ned E.

Powerful and Scalable, But Complex for Beginners

Likeliness to Recommend

Pros

Opeyemi B.

Why Businesses Are Moving to Microsoft Azure ML

Likeliness to Recommend

Pros

Sienna W.

Fruitful Machine Learning App

Likeliness to Recommend

Pros

Most Popular Microsoft Azure Machine Learning Comparisons

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1
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Feature Ratings

Data Exploration and Visualization

Data Pre-Processing

Model Training

Pre-Packaged AI/ML Services

Feature Engineering

Model Tuning

Model Monitoring and Management

Data Labeling

Algorithm Recommendation

Ensembling

Data Ingestion

Vendor Capability Ratings

Ease of Data Integration

Ease of IT Administration

Quality of Features

Ease of Implementation

Ease of Customization

Product Strategy and Rate of Improvement

Breadth of Features

Usability and Intuitiveness

Availability and Quality of Training

Business Value Created

Vendor Support