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Amazon

AWS Machine Learning

Composite Score

8.6 /10

CX Score

8.9 /10

67 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 Data Quadrant Champion

Latest Award, 2025

1 Top Rated Capabilities

1 Top Rated Features

Latest Award, 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

94 Plan to Renew

1
Since last award

81 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.

+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
Effective Service

Feature Ratings

Average 78

Performance and Scalability

Pre-Packaged AI/ML Services

Data Pre-Processing

Data Ingestion

Openness and Flexibility

Algorithm Diversity

Model Tuning

Algorithm Recommendation

Feature Engineering

Data Labeling

Model Training

Vendor Capability Ratings

Average 78

Quality of Features

Ease of Data Integration

Vendor Support

Ease of Implementation

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

Abiodun S.

Role: Industry Specific Role
Industry: Technology
Involvement: IT Leader or Manager

Validated Review

Verified Reviewer

Submitted May 2025

"A Powerful Tool for Machine Learning"

Likeliness to Recommend

8 /10

What differentiates AWS Machine Learning from other similar products?

Security and Compliance: Robust security features and compliance with industry standards, ensuring data protection and regulatory adherence. - Cost-Effective: Pay-as-you-go pricing model, allowing users to only pay for the resources used.

What is your favorite aspect of this product?

Support for Popular Frameworks: Support for popular machine learning frameworks, such as TensorFlow, PyTorch, and MXNet.

What do you dislike most about this product?

Data Preparation: Time-consuming data preparation and feature engineering processes.

What recommendations would you give to someone considering this product?

Plan for Integration: Think about how AWS Machine Learning will integrate with existing infrastructure, applications, and workflows.

Pros

Helps Innovate
Continually Improving Product
Trustworthy
Unique Features

Adebayo F.

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

Validated Review

Verified Reviewer

Submitted May 2025

Robust and Scalable Software for Machine Learning

Likeliness to Recommend

9 /10

What differentiates AWS Machine Learning from other similar products?

AWS Machine Learning stands out for its scalability, breadth of services, and deep integration with the broader AWS ecosystem, allowing users to build, train, and deploy models at any scale while leveraging powerful cloud infrastructure and data tools.

What is your favorite aspect of this product?

My favorite aspect of AWS Machine Learning is its scalability and flexibility—it supports everything from simple model deployment to complex, enterprise-level machine learning pipelines, all within a unified cloud environment.

What do you dislike most about this product?

What I dislike most about AWS Machine Learning is its complexity, as the platform can be overwhelming for newcomers, with a steep learning curve and sometimes scattered documentation across different services.

What recommendations would you give to someone considering this product?

I’d recommend AWS Machine Learning for users who need scalability and flexibility, but be prepared for a steep learning curve—take advantage of AWS tutorials, documentation, and community resources to navigate the platform and make the most of its capabilities.

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

Prince P.

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

Validated Review

Verified Reviewer

Submitted Apr 2025

Its is Very Effecient for ML tasks

Likeliness to Recommend

9 /10

What differentiates AWS Machine Learning from other similar products?

Easy of Use, Implemetatin and also its effeicency in performing ML jobs/task

What is your favorite aspect of this product?

Mostly all its features sits well with me

What do you dislike most about this product?

Nothing in particular

What recommendations would you give to someone considering this product?

Amazon Machine Learning is a robust solution for ML task, it like an all in one package and its very intuitive and user friendly

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

AWS Machine Learning

What is AWS Machine Learning?

Company Details

Need Assistance?

Awards & Recognition

2025 Data Quadrant Champion

2024 Emotional Footprint Champion

AWS Machine Learning Ratings

88 Likeliness to Recommend

94 Plan to Renew

1 Since last award

81 Satisfaction of Cost Relative to Value

1 Since last award

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

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

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.

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.

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.

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.

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.

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.

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

Abiodun S.

"A Powerful Tool for Machine Learning"

Likeliness to Recommend

Pros

Adebayo F.

Robust and Scalable Software for Machine Learning

Likeliness to Recommend

Pros

Prince P.

Its is Very Effecient for ML tasks

Likeliness to Recommend

Pros

Most Popular AWS Machine Learning Comparisons

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1
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Feature Ratings

Performance and Scalability

Pre-Packaged AI/ML Services

Data Pre-Processing

Data Ingestion

Openness and Flexibility

Algorithm Diversity

Model Tuning

Algorithm Recommendation

Feature Engineering

Data Labeling

Model Training

Vendor Capability Ratings

Quality of Features

Ease of Data Integration

Vendor Support

Ease of Implementation

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