Starting with the right use case
is key to organizational buy-in
In this eBook, we have outlined seven use cases in which Amazon Web Services (AWS)
customers have successfully applied ML. These use cases can strengthen your business case
for wider adoption of ML, and you can apply them to kick-start your ML journey or add them
to your current strategy.
What makes a good ML use case?
• Solves a real problem for your business—one that's important enough to get attention,
support, and adoption
• Increases performance, reduces costs, or improves your customer experience
• Includes technical experts to conduct feasibility assessments and domain experts to
ensure the solution will be used
• Can be completed in 6–8 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 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 ML tools you'll need
in a single service.
7 leading use cases:
1
Improve employee productivity ›
2
Automate document data extraction
and analysis ›
3
Add AI to any contact center ›
4
Improve customer self-service
experience ›
5
Personalize customer
recommendations ›
6
Automate content moderation ›
7
Validate user identity ›
3
