Microsoft Corporation

Microsoft Azure Machine Learning

Composite Score

8.7 /10

CX Score

9.0 /10

42 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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2024 Data Quadrant Gold Medalist

Latest Award, 2024

5 Top Rated Capabilities

9 Top Rated Features

Latest Award, 2024

2023 Emotional Footprint Champion

2023

Strategy and Innovation
Service Experience
Product Impact

2023

2023 Data Quadrant Gold Medalist

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

100 Plan to Renew

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.

+92 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?

0% Negative

3% Neutral

97% Positive

Pros

Performance Enhancing
Respectful
Includes Product Enhancements
Reliable

Feature Ratings

Average 85

Model Training

Data Exploration and Visualization

Pre-Packaged AI/ML Services

Data Pre-Processing

Feature Engineering

Model Tuning

Performance and Scalability

Data Labeling

Model Monitoring and Management

Data Ingestion

Explainability

Vendor Capability Ratings

Average 82

Breadth of Features

Ease of Data Integration

Quality of Features

Ease of Implementation

Ease of Customization

Availability and Quality of Training

Business Value Created

Ease of IT Administration

Product Strategy and Rate of Improvement

Usability and Intuitiveness

Vendor Support

Microsoft Azure Machine Learning Reviews

Yash l.

Role: Student Academic
Industry: Engineering
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted May 2021

"Easy to train and test model using drag and drop"

Likeliness to Recommend

10 /10

What differentiates Microsoft Azure Machine Learning from other similar products?

Microsoft Azure Machine Learning Studio has the best drag and drop functionalities. Jupyter Notebook platform has integrated with this studio so, end-user can write and execute their code easily which makes this studio more flexible as compared to other similar products. Here end-user can use the designer tool to train their machine learning model using drag and drop to the modules and dataset to create an ML flowchart. They also provide Data labeling features for labeling the dataset and have more capabilities as compared to other similar products.

What is your favorite aspect of this product?

My favorite aspect of this product is that they provide a no-code service to the end-user so, the end-user can easily train their model without writing any programming code. This product provides lots of algorithms. This product provides a specific API URL and API key so, the end-user can integrate their machine learning model with the application. It has the best user interface. Data visualization and Data labeling are the key features of the studio. Studio predicts the end-user data accurately. End-user can integrate their machine learning model with other Microsoft applications like Microsoft Excel so, the model predicts data from excel.

What do you dislike most about this product?

Storage space is limited and the subscription cost is too high. It takes lots of time while creating a machine learning model. Other than that all features and services are good.

What recommendations would you give to someone considering this product?

Microsoft Azure Machine Learning Studio is very easy to use. If end-user are enthusiastic about AIML the product is very useful for them, whether you are a beginner. End-user can easily train and predict their model so, go for it. The main thing that end-user should consider, you have to choose the subscription plan for using this studio. The features and service this product provides are good so, if you are looking for a studio that has all the AIML tools so, considering this product is valuable.

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

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

2024 Data Quadrant Gold Medalist

2023 Emotional Footprint Champion

2023 Data Quadrant Gold Medalist

Microsoft Azure Machine Learning Ratings

89 Likeliness to Recommend

100 Plan to Renew

85 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+92 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

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.

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

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

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.

Performance and Scalability Systems performance (time) and scalability end-to-end: from data load and processing to model creation and deployment.

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.

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.

Explainability Ability to explain/ visualize the model.

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

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

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.

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.

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

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.

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

Yash l.

"Easy to train and test model using drag and drop"

Likeliness to Recommend

Pros

Lindsay D.

Pricey but well worth the money

Likeliness to Recommend

Pros

Most Popular Microsoft Azure Machine Learning Comparisons

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1
Since last award

Feature Ratings

Model Training

Data Exploration and Visualization

Pre-Packaged AI/ML Services

Data Pre-Processing

Feature Engineering

Model Tuning

Performance and Scalability

Data Labeling

Model Monitoring and Management

Data Ingestion

Explainability

Vendor Capability Ratings

Breadth of Features

Ease of Data Integration

Quality of Features

Ease of Implementation

Ease of Customization

Availability and Quality of Training

Business Value Created

Ease of IT Administration

Product Strategy and Rate of Improvement

Usability and Intuitiveness

Vendor Support