Amazon

AWS Machine Learning

8.0 /10

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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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.

85 Likeliness to Recommend

89 Plan to Renew

74 Satisfaction of Cost Relative to Value

2
Since last award

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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?

0% Negative

9% Neutral

91% Positive

Pros

Reliable
Security Protects
Respectful
Continually Improving Product

Feature Ratings

Average 77

Performance and Scalability

Data Ingestion

Pre-Packaged AI/ML Services

Data Pre-Processing

Feature Engineering

Data Exploration and Visualization

Algorithm Recommendation

Ensembling

Data Labeling

Model Training

Model Tuning

Vendor Capability Ratings

Average 77

Ease of IT Administration

Product Strategy and Rate of Improvement

Breadth of Features

Business Value Created

Ease of Customization

Quality of Features

Usability and Intuitiveness

Vendor Support

Ease of Data Integration

Ease of Implementation

Availability and Quality of Training

AWS Machine Learning Reviews

Jocelyn B.

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

Validated Review

Verified Reviewer

Submitted Mar 2024

We are reinventing our processes with Amazon ML

Likeliness to Recommend

9 /10

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

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

Pradeep P.

Role: Information Technology
Industry: Transportation
Involvement: IT Development, Integration, and Administration

Validated Review

Verified Reviewer

Submitted Feb 2024

AWS is a powerful tool

Likeliness to Recommend

9 /10

What differentiates AWS Machine Learning from other similar products?

1. Innovative technologies 2. it is quite reliable and secure compared to other resources. 3. It is user friendly Graphical user interface

What is your favorite aspect of this product?

Highly cost effective AWS tools simplify tasks for developers. These include the AWS CLI, SDKs for languages, IDEs, and DevOps tools like AWS CloudFormation and AWS CodeDeploy. AWS introduces new technologies through research like AI, ML, serverless computing, containers, and edge computing.

What do you dislike most about this product?

1. Meeting regulatory rules like GDPR, HIPAA, or PCI DSS with AWS services can be tough. Making certain compliance may mean extra work and money putting in and overseeing security, checking, and papers. 2.Storing sensitive data online raises concerns. AWS uses security like encryption but users manage data. 3.Occasional downtime can occur on AWS. Disruptions may impact businesses relying on AWS, affecting work and customer experience.

What recommendations would you give to someone considering this product?

Must use web service tool .

Pros

Continually Improving Product
Performance Enhancing
Enables Productivity
Inspires Innovation

AWS Machine Learning

What is AWS Machine Learning?

Company Details

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Awards & Recognition

AWS Machine Learning Ratings

85 Likeliness to Recommend

89 Plan to Renew

74 Satisfaction of Cost Relative to Value

2 Since last award

Emotional Footprint Overview

+83 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

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

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.

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

Data Pre-Processing Ability to wrangle or pre-process data to ready it for model training.

Feature Engineering Ability to extract and build features from raw data.

Data Exploration and Visualization Ability to visually manipulate, interact with, and explore data, and perform basic descriptive statistics and pattern detection.

Algorithm Recommendation Ability to recommend a best ML algorithm to use based on dataset characteristics.

Ensembling 'Ability to combine ML models for best results.

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.

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

Vendor Capability Ratings Average rating of all vendor capabilities, some of which may not be shown below

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.

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.

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.

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.

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.

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.

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.

Ease of Data Integration Use this data to determine whether the product will cause headaches or make data integration easy.

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.

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

Jocelyn B.

We are reinventing our processes with Amazon ML

Likeliness to Recommend

Pros

Ekta S.

Very comprehensive product: good to use

Likeliness to Recommend

Pros

Pradeep P.

AWS is a powerful tool

Likeliness to Recommend

Pros

Most Popular AWS Machine Learning Comparisons

Google Cloud Vertex AI

MathWorks Matlab

Microsoft Azure Machine Learning

Alteryx

Databricks Data Intelligence Platform

DataRobot AI Platform

2
Since last award

Feature Ratings

Performance and Scalability

Data Ingestion

Pre-Packaged AI/ML Services

Data Pre-Processing

Feature Engineering

Data Exploration and Visualization

Algorithm Recommendation

Ensembling

Data Labeling

Model Training

Model Tuning

Vendor Capability Ratings

Ease of IT Administration

Product Strategy and Rate of Improvement

Breadth of Features

Business Value Created

Ease of Customization

Quality of Features

Usability and Intuitiveness

Vendor Support

Ease of Data Integration

Ease of Implementation

Availability and Quality of Training