Starting with the right use case
is key to organizational buy-in
In this eBook, we have outlined seven use cases where AWS customers
have successfully applied machine learning. These use cases will
strengthen your business case for wider adoption of machine learning,
and you can apply them to kick-start your machine learning journey or
add them to your current strategy.
What makes a good machine learning use case?
• Solves a real problem for your business—one that's important
enough to get attention, support, and adoption
• Leverages sources of untapped data
• Increases performance, reduces costs, and/or improves your end-
customer experiences
• Includes technical experts to conduct feasibility assessments and
domain experts to ensure the solution will be used
• Can be completed in 6–10 months
When you are ready to deploy your use case, you have the choice
of using one or more fully-managed AWS AI Services to quickly
get started and easily integrate intelligence into your applications.
Or, if you want to develop your own models, you can use Amazon
SageMaker—a solution that provides you with all the machine learning
tools you'll need in a single service.
7
leading use cases
Improve employee productivity ›
Automate document data extraction and analysis ›
Add contact center intelligence ›
Personalize customer recommendations ›
Increase the value of media assets ›
Improve business operations with forecasting ›
Identify fraudulent online activities ›
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