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Microsoft Corporation

Microsoft Azure Machine Learning

Composite Score

8.9 /10

CX Score

9.1 /10

70 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.

Company Details

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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

94 Plan to Renew

6
Since last award

85 Satisfaction of Cost Relative to Value

1
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
Reliable
Security Protects
Enables Productivity

Feature Ratings

Average 84

Pre-Packaged AI/ML Services

Model Training

Data Exploration and Visualization

Data Pre-Processing

Model Tuning

Feature Engineering

Algorithm Recommendation

Data Labeling

Model Monitoring and Management

Ensembling

Algorithm Diversity

Vendor Capability Ratings

Average 83

Ease of Data Integration

Ease of Customization

Breadth of Features

Quality of Features

Product Strategy and Rate of Improvement

Ease of IT Administration

Ease of Implementation

Usability and Intuitiveness

Availability and Quality of Training

Business Value Created

Vendor Support

Microsoft Azure Machine Learning Reviews

Jay S.

Role: Information Technology
Industry: Technology
Involvement: IT Development, Integration, and Administration

Validated Review

Verified Reviewer

Submitted Feb 2024

It is good for use . It also time efficient.

Likeliness to Recommend

9 /10

What differentiates Microsoft Azure Machine Learning from other similar products?

AWS machine learning

What is your favorite aspect of this product?

Delightful

What do you dislike most about this product?

Very good product

What recommendations would you give to someone considering this product?

Use it mostly because it time efficient

Pros

Performance Enhancing
Enables Productivity
Trustworthy
Unique Features

Ramy s.

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

Validated Review

Verified Reviewer

Submitted Feb 2024

User-friendly Machine Learning Service.

Likeliness to Recommend

8 /10

What differentiates Microsoft Azure Machine Learning from other similar products?

Azure Machine Learning integrates seamlessly with the Azure ecosystem tools and services. It provides a user-friendly drag-and-drop visual designer that helps average users develop and run advanced models within a low-code environment with minimal coding experience.

What is your favorite aspect of this product?

Azure ML is easy to use, has automatic model retraining capabilities, supports various programming languages/frameworks, and integrates with commonly used open-source data science and machine learning libraries.

What do you dislike most about this product?

While Azure Machine Learning is an easy-to-use service, it usually gets more complex as the project requires more customizations and more experienced AI, data, and Azure Cloud professionals to customize and optimize the Machine Learning workload on Azure.

What recommendations would you give to someone considering this product?

I would recommend spending enough time learning about Azure ML features, limitations, and cost and also a high-level overview of Azure Cloud service through the Azure architecture center and the official service documentation.

Pros

Helps Innovate
Continually Improving Product
Reliable
Enables Productivity

Amanda D.

Role: Operations
Industry: Technology
Involvement: IT Development, Integration, and Administration

Validated Review

Verified Reviewer

Submitted Jan 2024

Can’t go wrong with MS

Likeliness to Recommend

10 /10

What differentiates Microsoft Azure Machine Learning from other similar products?

More creative elements

What is your favorite aspect of this product?

Love the ease of editing

What do you dislike most about this product?

Publishing is sometimes difficukt

What recommendations would you give to someone considering this product?

MS is a reliable Brand

Pros

Continually Improving Product
Reliable
Trustworthy
Efficient Service

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

94 Plan to Renew

6 Since last award

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

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

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

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

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

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.

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

Ensembling 'Ability to combine ML models for best results.

Algorithm Diversity Provides multiple diverse ready-to-use machine learning algorithms out-of-the-box.

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.

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.

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.

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

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

Jay S.

It is good for use . It also time efficient.

Likeliness to Recommend

Pros

Ramy s.

User-friendly Machine Learning Service.

Likeliness to Recommend

Pros

Amanda D.

Can’t go wrong with MS

Likeliness to Recommend

Pros

Most Popular Microsoft Azure Machine Learning Comparisons

Google Cloud Vertex AI

MathWorks MATLAB

AWS Machine Learning

Databricks Data Intelligence Platform

Altair RapidMiner

DataRobot AI Platform

1
Since last award

6
Since last award

1
Since last award

Feature Ratings

Pre-Packaged AI/ML Services

Model Training

Data Exploration and Visualization

Data Pre-Processing

Model Tuning

Feature Engineering

Algorithm Recommendation

Data Labeling

Model Monitoring and Management

Ensembling

Algorithm Diversity

Vendor Capability Ratings

Ease of Data Integration

Ease of Customization

Breadth of Features

Quality of Features

Product Strategy and Rate of Improvement

Ease of IT Administration

Ease of Implementation

Usability and Intuitiveness

Availability and Quality of Training

Business Value Created

Vendor Support