Amazon

AWS Machine Learning

Composite Score

8.5 /10

CX Score

8.7 /10

60 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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2024 Emotional Footprint Champion

Latest Award, 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

1
Since last award

93 Plan to Renew

1
Since last award

79 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.

+89 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

6% Neutral

94% Positive

Pros

Continually Improving Product
Respectful
Efficient Service
Security Protects

Feature Ratings

Average 78

Pre-Packaged AI/ML Services

Performance and Scalability

Data Pre-Processing

Data Ingestion

Openness and Flexibility

Algorithm Recommendation

Feature Engineering

Model Tuning

Algorithm Diversity

Data Labeling

Model Training

Vendor Capability Ratings

Average 78

Quality of Features

Ease of Implementation

Ease of Data Integration

Vendor Support

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

Ekta S.

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

Validated Review

Verified Reviewer

Submitted Mar 2024

Very comprehensive product: good to use

Likeliness to Recommend

10 /10

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

Validated Review

Anonymous Reviewer

Submitted Mar 2024

It is scalable, and advantage to integrate.

Likeliness to Recommend

9 /10

Pros

Reliable
Effective Service
Respectful
Client Friendly Policies

Ketan S.

Role: Information Technology
Industry: Engineering
Involvement: IT Leader or Manager

Validated Review

Verified Reviewer

Submitted Mar 2024

Good ML services but cost of tool is very high

Likeliness to Recommend

5 /10

What differentiates AWS Machine Learning from other similar products?

1. Have a very huge community and variety of AI/ML services to build an AI/ML solution 2. Platform is very intuitive and easy to manage

What is your favorite aspect of this product?

majorly used sagemaker, it is a very good tool to deploy an ML model very easily. It provide variety of services which makes it easier to build the model.

What do you dislike most about this product?

Does not provide any good data labelling services. Don't have any Ai recommendation service inbuilt

What recommendations would you give to someone considering this product?

If your requirement is to build an model quickly and deploy it, then definitely go for it but there will be a learning curve for services to use.

Pros

Continually Improving Product
Reliable
Enables Productivity
Inspires Innovation

Cons

Vendor Friendly Policies
Commodity Features
Less Caring

AWS Machine Learning

What is AWS Machine Learning?

Company Details

Need Assistance?

Awards & Recognition

2024 Emotional Footprint Champion

AWS Machine Learning Ratings

88 Likeliness to Recommend

1 Since last award

93 Plan to Renew

1 Since last award

79 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+89 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

Pre-Packaged AI/ML Services Pre-trained models and associated resources, e.g., for forecasting, recommendations, image and video analysis, NLP, voice, etc.

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

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 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.

Model Tuning 'Ability to fine-tune models based on monitoring feedback within the platform.

Algorithm Diversity Provides multiple diverse ready-to-use machine learning algorithms out-of-the-box.

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 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.

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.

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

Ekta S.

Very comprehensive product: good to use

Likeliness to Recommend

Pros

It is scalable, and advantage to integrate.

Likeliness to Recommend

Pros

Ketan S.

Good ML services but cost of tool is very high

Likeliness to Recommend

Pros

Cons

Most Popular AWS Machine Learning Comparisons

Google Cloud Vertex AI

MathWorks MATLAB

Microsoft Azure Machine Learning

Databricks Data Intelligence Platform

DataRobot AI Platform

KNIME Analytics Platform

1
Since last award

1
Since last award

1
Since last award

Feature Ratings

Pre-Packaged AI/ML Services

Performance and Scalability

Data Pre-Processing

Data Ingestion

Openness and Flexibility

Algorithm Recommendation

Feature Engineering

Model Tuning

Algorithm Diversity

Data Labeling

Model Training

Vendor Capability Ratings

Quality of Features

Ease of Implementation

Ease of Data Integration

Vendor Support

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