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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Real user data aggregated to summarize the product performance and customer experience.
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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.
87 Likeliness to Recommend
1
Since last award
91 Plan to Renew
3
Since last award
81 Satisfaction of Cost Relative to Value
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?
Pros
- Continually Improving Product
- Respectful
- Efficient Service
- Includes Product Enhancements
How to read the Emotional Footprint
The Net Emotional Footprint measures high-level user sentiment towards particular product offerings. It aggregates emotional response ratings for various dimensions of the vendor-client relationship and product effectiveness, creating a powerful indicator of overall user feeling toward the vendor and product.
While purchasing decisions shouldn't be based on emotion, it's valuable to know what kind of emotional response the vendor you're considering elicits from their users.
Footprint
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Feature Ratings
Pre-Packaged AI/ML Services
Performance and Scalability
Data Pre-Processing
Openness and Flexibility
Data Ingestion
Algorithm Diversity
Algorithm Recommendation
Feature Engineering
Model Tuning
Model Training
Data Labeling
Vendor Capability Ratings
Quality of Features
Ease of Data Integration
Ease of Implementation
Vendor Support
Business Value Created
Breadth of Features
Ease of IT Administration
Ease of Customization
Product Strategy and Rate of Improvement
Usability and Intuitiveness
Availability and Quality of Training
AWS Machine Learning Reviews
Monika C.
- Role: Information Technology
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Apr 2024
Likeliness to Recommend
Pros
- Continually Improving Product
- Reliable
- Enables Productivity
- Unique Features
Ansh K.
- Role: Information Technology
- Industry: Education
- Involvement: IT Leader or Manager
Submitted Apr 2024
Sure to kill in the market
Likeliness to Recommend
What differentiates AWS Machine Learning from other similar products?
Comprehensive set of AI and resources, enabling users to gain deeper insights from their data
What is your favorite aspect of this product?
AWS provides support at every stage of the machine learning
What do you dislike most about this product?
Inconsistency between the services provided
What recommendations would you give to someone considering this product?
Can go used this product as it's beginner friendly and have greater aspect also in the future
Pros
- Helps Innovate
- Reliable
- Enables Productivity
- Trustworthy
Aman k.
- Role: Information Technology
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Dec 2025
Scalable, reliable ML platform.
Likeliness to Recommend
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
