Mackenzie is the Global Startup Evangelist at AWS. His days are spent traveling the globe to meet startups, share their stories, and connect engineering teams together. Every day there are a large number of startups launching on AWS across every imaginable industry. It’s Mackenzie’s mission to find stories of startups that are helping to improve the world and share these stories with a wide audience.
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Prior to joining AWS, Mackenzie was the Head of Technical Operations at Betterment, the world’s largest independent robo-advisor based in NYC which manages over $8B in assets. Mackenzie was a founding engineer and Head of Technical Operations at Oscar Health, an insurance startup also based in NYC, helping to grow the company to over 400+ employees.
Join this workshop to get started implementing your own generative AI (GenAI) applications to drive your business outcomes. Learn the basics of GenAI, use cases for it in the Public Sector, and operational/cost optimization best practices when implementing GenAI. The workshop will contain demos to show how services such as Amazon Bedrock and Amazon SageMaker can be used to implement Generative AI applications such as chatbots and image/document generation.
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A hands-on experience with Amazon SageMaker will provide education on how to use existing foundation models, how to use prompt engineering to improve the output of the foundational models, and how to fine-tune these foundation models to meet your needs.Â
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Temporary AWS accounts will be provided for the hands-on labs.Â
The content of this event will be beneficial to business decision-makers, data scientists, and developers interested in learning how to utilize AWS services in their approach to generative AI.
Presentation: Introduction to generative AI on AWS
1:00PM - 2:30PM EDT
- Building with Generative AI on AWS
- Prompt Engineering
- Well-Architected AI/ML
- Generative AI demos
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Lab: Hands-on experience with Amazon SageMaker
2:30PM - 5:00PM EDT
- Analyze the impact of Prompt Engineering
- Fine-tune a Foundation Model for dialogue summarization
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