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.
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Real user data aggregated to summarize the product performance and customer experience.
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85 Likeliness to Recommend
89 Plan to Renew
75 Satisfaction of Cost Relative to Value
3
Since last award
Emotional Footprint Overview
+83 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
- Reliable
- Security Protects
- Respectful
- Continually Improving Product
Emotional Footprint
How to Read
Positive
Neutral
Negative
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
Feature Ratings
Performance and Scalability
Pre-Packaged AI/ML Services
Data Ingestion
Data Pre-Processing
Feature Engineering
Model Training
Algorithm Recommendation
Data Exploration and Visualization
Ensembling
Data Labeling
Model Tuning
Vendor Capability Ratings
Ease of Data Integration
Quality of Features
Business Value Created
Breadth of Features
Ease of Implementation
Vendor Support
Product Strategy and Rate of Improvement
Ease of Customization
Usability and Intuitiveness
Ease of IT Administration
Availability and Quality of Training
AWS Machine Learning Reviews
- Role: Information Technology
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Mar 2024
Easy to use, but a bit pricey. features are good.
Likeliness to Recommend
Pros
- Helps Innovate
- Continually Improving Product
- Reliable
- Performance Enhancing
Jocelyn B.
- Role: Consultant
- Industry: Technology
- Involvement: End User of Application
Submitted Mar 2024
We are reinventing our processes with Amazon ML
Likeliness to Recommend
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
Ekta S.
- Role: Information Technology
- Industry: Technology
- Involvement: End User of Application
Submitted Mar 2024
Very comprehensive product: good to use
Likeliness to Recommend
What differentiates AWS Machine Learning from other similar products?
The ensembling capability of model training along with pretrained models provided by AWS are a gamechanger. AWS ML techniques are way ahead of GCP and Azure in its functionalities and model capabilities. Sagemaker Jumpstart, Inference recommender are techniques which are the leading differentiators.
What is your favorite aspect of this product?
The Sagemaker studio is an all in one portal to go to whenever we wish to work with any ML capabilities. It provides integration with Gen AI capabilities as well. It has all the latest models integrated within itself which makes it a comprehensive product to use.
What do you dislike most about this product?
I had used AWS comprehend for developing models for one such application. AWS comprehend has pretrained models for training text data for various tasks. I see that it has limited capacity for model training and experimentation. If there would be more flexibility for hyper parameter tuning it would be worth using it then.
What recommendations would you give to someone considering this product?
AWS ML is an end to end service which can be used to work with all kinds of workloads. It is very useful and reliable service to use. Worth exploring the pretrained models which are bundled as a part of various services. It has all aspects of ML bundled up together in the best suite possible. If you are starting then start with basic models and then scale up further.
Pros
- Performance Enhancing
- Trustworthy
- Unique Features
- Effective Service