Write a review temporarily disabled. Our system flagged this request as potentially automated and has disabled form submission for security reasons. If you believe this is a mistake, please contact us. We’ll be happy to help resolve the issue.

Amazon

AWS Machine Learning

Composite Score

8.6 /10

CX Score

8.9 /10

77 Reviews Write a Review

Claim Software

What is AWS Machine Learning?

Amazon Machine Learning is an Amazon Web Services product that allows a developer to discover patterns in end-user data through algorithms, construct mathematical models based on these patterns and then create and implement predictive applications.

Company Details

Need Assistance?

We're here to help you with understanding our reports and the data inside to help you make decisions.

Get Assistance

Awards & Recognition

AWS Machine Learning won the following awards in the Machine Learning Platforms category

Filter By

2025 Emotional Footprint Champion

Latest Award, 2025

2025 Data Quadrant Champion

2025

1 Top Rated Capabilities

1 Top Rated Features

2025

2024 Emotional Footprint Champion

2024

0 Top Rated Capabilities

2 Top Rated Features

2024

0 Top Rated Capabilities

1 Top Rated Features

2023

AWS 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 AWS 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.

88 Likeliness to Recommend

92 Plan to Renew

2
Since last award

82 Satisfaction of Cost Relative to Value

1
Since last award

{y}

{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.

+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 AWS Machine Learning?

0% Negative

4% Neutral

96% Positive

Pros

Respectful
Efficient Service
Effective Service
Includes Product Enhancements

Feature Ratings

Average 79

Pre-Packaged AI/ML Services

Performance and Scalability

Data Pre-Processing

Openness and Flexibility

Algorithm Diversity

Model Tuning

Feature Engineering

Data Ingestion

Algorithm Recommendation

Data Labeling

Model Training

Vendor Capability Ratings

Average 79

Quality of Features

Ease of Data Integration

Business Value Created

Vendor Support

Ease of Implementation

Breadth of Features

Ease of Customization

Product Strategy and Rate of Improvement

Ease of IT Administration

Usability and Intuitiveness

Availability and Quality of Training

AWS Machine Learning Reviews

Rushikesh G.

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

Validated Review

Verified Reviewer

Submitted Jul 2021

vast variety of updates

Likeliness to Recommend

10 /10

What differentiates AWS Machine Learning from other similar products?

AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist and expert practitioner

What is your favorite aspect of this product?

Intelligent document processing

What do you dislike most about this product?

High Level of Error Susceptibility

What recommendations would you give to someone considering this product?

Yes, It is good in the market

Pros

Performance Enhancing
Fair
Unique Features
Altruistic

AWS Machine Learning

What is AWS Machine Learning?

Company Details

Need Assistance?

Awards & Recognition

2025 Emotional Footprint Champion

2025 Data Quadrant Champion

2024 Emotional Footprint Champion

AWS Machine Learning Ratings

88 Likeliness to Recommend

92 Plan to Renew

2 Since last award

82 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+92 Net Emotional Footprint

How much do users love AWS 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.

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

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

Openness and Flexibility Integration with open-source and 3P resources (languages, frameworks, libraries, etc.)

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

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.

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.

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

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

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

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.

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.

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 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.

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

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.

AWS Machine Learning Reviews

Rushikesh G.

vast variety of updates

Likeliness to Recommend

Pros

Most Popular AWS Machine Learning Comparisons

Gemini Enterprise Agent Platform

MathWorks MATLAB

Microsoft Azure Machine Learning

Databricks Data Intelligence Platform

Altair RapidMiner

DataRobot AI Platform

2
Since last award

1
Since last award

Feature Ratings

Pre-Packaged AI/ML Services

Performance and Scalability

Data Pre-Processing

Openness and Flexibility

Algorithm Diversity

Model Tuning

Feature Engineering

Data Ingestion

Algorithm Recommendation

Data Labeling

Model Training

Vendor Capability Ratings

Quality of Features

Ease of Data Integration

Business Value Created

Vendor Support

Ease of Implementation

Breadth of Features

Ease of Customization

Product Strategy and Rate of Improvement

Ease of IT Administration

Usability and Intuitiveness

Availability and Quality of Training