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Amazon

AWS Machine Learning

Composite Score

8.6 /10

CX Score

8.9 /10

71 Reviews Write a Review

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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

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Awards & Recognition

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

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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

93 Plan to Renew

1
Since last award

81 Satisfaction of Cost Relative to Value

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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.

+91 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

5% Neutral

95% Positive

Pros

Continually Improving Product
Respectful
Efficient Service
Includes Product Enhancements

Feature Ratings

Average 78

Pre-Packaged AI/ML Services

Performance and Scalability

Data Ingestion

Data Pre-Processing

Algorithm Diversity

Openness and Flexibility

Model Tuning

Algorithm Recommendation

Feature Engineering

Model Training

Data Labeling

Vendor Capability Ratings

Average 78

Quality of Features

Ease of Implementation

Ease of Data Integration

Vendor Support

Business Value Created

Breadth of Features

Ease of Customization

Ease of IT Administration

Product Strategy and Rate of Improvement

Usability and Intuitiveness

Availability and Quality of Training

AWS Machine Learning Reviews

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

Validated Review

Anonymous Reviewer

Submitted Mar 2024

Easy to use, but a bit pricey. features are good.

Likeliness to Recommend

9 /10

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

Jocelyn B.

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

Validated Review

Verified Reviewer

Submitted Mar 2024

We are reinventing our processes with Amazon ML

Likeliness to Recommend

9 /10

What do you dislike most about this product?

Amazon makes it difficult and cumbersome for teams, department or organizations already locked into using the AWS machine learning platform, to switch to other providers. This is due to a variety of factors , including existing dependencies , which makes it tedious to migrate to substitute cloud platforms without having to rewrite code or fully redesign systems.

What recommendations would you give to someone considering this product?

I think it is important for the leadership or management team of an organisation seeking to adopt AWS Machine learning, to have a fair understanding of the common business and technical problems they seek to solve with it ,before adoption.

Pros

Reliable
Performance Enhancing
Enables Productivity
Security Protects

Aman k.

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

Validated Review

Verified Reviewer

Submitted Dec 2025

Scalable, reliable ML platform.

Likeliness to Recommend

9 /10

What differentiates AWS Machine Learning from other similar products?

AWS Machine Learning stands out because it integrates smoothly across the entire AWS ecosystem. You can go from data ingestion to model deployment without changing platforms, which saves a lot of time. The ability to train and deploy models on large datasets without worrying about infrastructure is also a significant advantage. The continuous pace of innovation, particularly with their integrations across AI and serverless services, keeps the platform ahead of many competitors.

What is your favorite aspect of this product?

My favorite aspect is how easy it is to scale experiments from small prototypes to full production workloads. SageMaker takes care of a lot of the hard work, including training jobs, tuning, and deployment. This allows me to focus more on the actual modeling work instead of worrying about the infrastructure. The ability to integrate with other AWS services is also very helpful.

What do you dislike most about this product?

The biggest drawback is that some features can feel complicated or need several steps to set up. The learning curve is steep, especially for those who are new to AWS. Some parts of the interface could be easier to navigate, and handling all the permissions across services can become confusing at times.

What recommendations would you give to someone considering this product?

I recommend spending some time understanding the main AWS services before exploring the ML tools. This will make the experience much smoother. Start small and experiment with the built-in notebooks. Gradually move toward production workflows. If your team works with large datasets or needs dependable scaling, AWS ML is a good choice. Just be ready to invest some time upfront to learn the platform well.

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

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

93 Plan to Renew

1 Since last award

81 Satisfaction of Cost Relative to Value

Emotional Footprint Overview

+91 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 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.

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

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

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

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

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

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

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

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

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

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.

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.

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.

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.

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

Easy to use, but a bit pricey. features are good.

Likeliness to Recommend

Pros

Jocelyn B.

We are reinventing our processes with Amazon ML

Likeliness to Recommend

Pros

Aman k.

Scalable, reliable ML platform.

Likeliness to Recommend

Pros

Most Popular AWS Machine Learning Comparisons

Google Cloud Vertex AI

MathWorks MATLAB

Microsoft Azure Machine Learning

Databricks Data Intelligence Platform

DataRobot AI Platform

Alteryx

1
Since last award

Feature Ratings

Pre-Packaged AI/ML Services

Performance and Scalability

Data Ingestion

Data Pre-Processing

Algorithm Diversity

Openness and Flexibility

Model Tuning

Algorithm Recommendation

Feature Engineering

Model Training

Data Labeling

Vendor Capability Ratings

Quality of Features

Ease of Implementation

Ease of Data Integration

Vendor Support

Business Value Created

Breadth of Features

Ease of Customization

Ease of IT Administration

Product Strategy and Rate of Improvement

Usability and Intuitiveness

Availability and Quality of Training