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.
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
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
Negative
Neutral
Positive
Feature Ratings
Pre-Packaged AI/ML Services
Performance and Scalability
Data Ingestion
Data Pre-Processing
Openness and Flexibility
Algorithm Diversity
Model Tuning
Feature Engineering
Algorithm Recommendation
Data Labeling
Model Training
Vendor Capability Ratings
Quality of Features
Vendor Support
Ease of Data Integration
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
Aakanksha K.
- Role: Information Technology
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Oct 2025
Powerful and Comprehensive ML Platform
Likeliness to Recommend
What differentiates AWS Machine Learning from other similar products?
The differences are its end-to-end coverage of the ML lifecycle, from data preparation to deployment. Its deep integration with AWS services like S3, Lambda, Redshift, and Glue makes workflows seamless and scalable. The platform offers flexibility to use custom models and multiple frameworks like TensorFlow, PyTorch, and Scikit-learn, while providing pre-built AI/ML services like Rekognition, Comprehend, and Bedrock for rapid deployment. Strong security, enterprise-grade compliance, and continuous innovation further set it apart, making it a versatile solution for both experimentation and production workloads.
What is your favorite aspect of this product?
My favorite aspect of AWS Machine Learning is how seamlessly it integrates with services like S3, Lambda, and SageMaker, making the entire process of building, training, and deploying models smooth and efficient. I also love its flexibility — whether using pre-built AI tools or custom ML frameworks, it gives both beginners and experts the freedom to experiment, innovate, and scale effortlessly.
What do you dislike most about this product?
What I dislike most about AWS Machine Learning is its complex pricing structure. It can be difficult to estimate total costs since charges depend on multiple factors like compute time, data storage, and specific service usage. Additionally, the platform’s vast range of tools can be overwhelming for beginners, requiring a steep learning curve before you can use it effectively. Better cost visibility and simplified onboarding would make the experience much smoother.
What recommendations would you give to someone considering this product?
If you’re considering AWS Machine Learning, I’d recommend starting small — experiment with AWS SageMaker first to understand the workflow and pricing model. Take advantage of AWS’s documentation and training resources; they’re extremely helpful for getting comfortable with the ecosystem. Make sure you plan your architecture and cost strategy in advance, as pricing can add up quickly depending on your usage. Also, integrate with other AWS services like S3 and Lambda for maximum efficiency. Overall, AWS ML is a powerful and scalable platform — perfect if you want flexibility, security, and enterprise-level performance.
Pros
- Continually Improving Product
- Reliable
- Security Protects
- Helps Innovate
Sienna W.
- Role: Information Technology
- Industry: Engineering
- Involvement: End User of Application
Submitted Sep 2025
Amazing Solution from the App
Likeliness to Recommend
What differentiates AWS Machine Learning from other similar products?
We obtain resourceful knowledge from this machine learning app The credibility of every analytical process is fully supported
What is your favorite aspect of this product?
The commitment in managing systems and business process is something the software has always supported The credibility of the customer help and insight analytics
What do you dislike most about this product?
This is the authentic platform that helps us manage every system and data throughout Only issue is on integration, which is a big problem
What recommendations would you give to someone considering this product?
We gain quality support from the software, and I would recommend
Pros
- Helps Innovate
- Efficient Service
- Caring
- Client Friendly Policies
Abiodun S.
- Role: Industry Specific Role
- Industry: Technology
- Involvement: IT Leader or Manager
Submitted May 2025
"A Powerful Tool for Machine Learning"
Likeliness to Recommend
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
