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
Atharva V.
- Role: Information Technology
- Industry: Technology
- Involvement: End User of Application
Submitted May 2024
Unlocking Potential with Advanced AWS Features
Likeliness to Recommend
What differentiates AWS Machine Learning from other similar products?
AWS Machine Learning differentiates itself by providing scalability and access to a wide range of cloud services while integrating easily into the larger AWS ecosystem. Because of its simple UI and detailed documentation, developers looking to innovate and improve their AI/ML projects can consider it as a great solution.
What is your favorite aspect of this product?
My favorite aspect of AWS Machine Learning as an AI/ML developer is its easy scaling and connectivity with other AWS services. This makes deployment and development easier, allowing me time to concentrate on problem-solving instead of infrastructure management.
What do you dislike most about this product?
The primary drawback of AWS Machine Learning for an independent developer is the possibility of rising costs with increased usage. Also, implementing all of it can be challenging and requires an in-depth knowledge of AWS services.
What recommendations would you give to someone considering this product?
Before implementing it directly go through its documentation and the training that AWS provide on their official platform, I personally would recommend to do the AWS training and certification before directly implementing it.
Pros
- Helps Innovate
- Continually Improving Product
- Reliable
- Unique Features
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
- Role: Information Technology
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Mar 2024
It is scalable, and advantage to integrate.
Likeliness to Recommend
Pros
- Reliable
- Effective Service
- Respectful
- Client Friendly Policies
