Starting with the right
use case is key
In this eBook, we have outlined seven use cases where AWS startup customers have
successfully applied ML to meet their goals.
What benefits can you expect from implementing ML?
• Solves a real problem for your business—one that's important enough to get
attention, support, and adoption
• Increases performance, reduces costs, and improves your customer experience
• Can be completed quickly—often in a few hours or a few weeks, depending
on the complexity
When you're ready, 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—an end-to-end solution that provides you with all the tools you'll need to
build your own ML models in a single service.
Your startup can scale and get to market faster with
AWS low-code ML tools for automatically building
and training models and no-code tools with pre-
built ML solutions. Practice MLOps and put in place
a collaborative and streamlined approach to the ML
development lifecycle.
Seven leading use cases
1 Automate document data extraction
and analysis ›
2 Personalize customer recommendations ›
3 Get more out of images and videos ›
4 Optimized sales and support ›
5 Forecast key business metrics ›
6 Validate user identity ›
7 Improve the customer self-service
experience ›
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