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Microsoft Corporation

Microsoft Azure Machine Learning

Composite Score

8.9 /10

CX Score

9.1 /10

64 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.

89 Likeliness to Recommend

1
Since last award

98 Plan to Renew

2
Since last award

84 Satisfaction of Cost Relative to Value

2
Since last award

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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

1% Neutral

97% Positive

Pros

Performance Enhancing
Respectful
Enables Productivity
Reliable

Feature Ratings

Average 85

Data Exploration and Visualization

Model Training

Data Pre-Processing

Pre-Packaged AI/ML Services

Model Tuning

Feature Engineering

Model Monitoring and Management

Algorithm Recommendation

Data Labeling

Ensembling

Data Ingestion

Vendor Capability Ratings

Average 83

Ease of Data Integration

Ease of Customization

Ease of IT Administration

Quality of Features

Product Strategy and Rate of Improvement

Breadth of Features

Ease of Implementation

Availability and Quality of Training

Usability and Intuitiveness

Business Value Created

Vendor Support

Microsoft Azure Machine Learning Reviews

Lindsay D.

Role: Operations
Industry: Healthcare
Involvement: Vendor Selection and Purchasing

Validated Review

Verified Reviewer

Submitted Jan 2020

Pricey but well worth the money

Likeliness to Recommend

8 /10

What differentiates Microsoft Azure Machine Learning from other similar products?

It’s features and algorhythms

What is your favorite aspect of this product?

Cost effective

What do you dislike most about this product?

Nothing in particular

What recommendations would you give to someone considering this product?

Be patient and fully train yourself

Pros

Continually Improving Product
Reliable
Enables Productivity
Trustworthy

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

89 Likeliness to Recommend

1 Since last award

98 Plan to Renew

2 Since last award

84 Satisfaction of Cost Relative to Value

2 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.

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

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

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

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

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

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

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

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

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 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.

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.

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.

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.

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.

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.

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

Lindsay D.

Pricey but well worth the money

Likeliness to Recommend

Pros

Most Popular Microsoft Azure Machine Learning Comparisons

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Feature Ratings

Data Exploration and Visualization

Model Training

Data Pre-Processing

Pre-Packaged AI/ML Services

Model Tuning

Feature Engineering

Model Monitoring and Management

Algorithm Recommendation

Data Labeling

Ensembling

Data Ingestion

Vendor Capability Ratings

Ease of Data Integration

Ease of Customization

Ease of IT Administration

Quality of Features

Product Strategy and Rate of Improvement

Breadth of Features

Ease of Implementation

Availability and Quality of Training

Usability and Intuitiveness

Business Value Created

Vendor Support