Solve common business problems with AI/ML
summary
Cloud-era AI moves from proofs to production with concrete tools and measured impact: use Amazon SageMaker for end-to-end model building, Amazon Fraud Detector to cut manual reviews from 10% to 1%, and Monitron/Lookout for Equipment for predictive maintenance. Customers report results like Ryanair’s 3 million chatbot conversations, Anthem raising automation from 80% to 90%, Bundesliga boosting article reads by 67%, a grocery chain cutting waste 30%, and Foxconn improving forecast accuracy 8%, showing these services actually run and scale.
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chapters (15)
- 0:00Intro
- 1:24What is it?
- 3:39How AWS is democratizing Al
- 6:50Amazon machine learning innovation at scale
- 12:28Add Al to your contact center workflow
- 15:22Key IDP use cases across Industries
- 20:39Amazon Personalize successes
- 21:14Fraud detection challenges and use cases
- 22:49Fraud detection customers
- 24:07Identity verification customers
- 26:08AWS ML forecasting customers
- 28:52Amazon Kendra customers
- 29:05Al use cases for Industrial customers
- 30:25Reduce downtime with end-to-end machine monitoring with Amazon Monitron
- 32:02Making It real AWS
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