Topic TBD

Given the excitement around Generative AI wanted to share AWS’s perspective on this topic. Amazon has been doing ML for over 20+ years and we see it everyday operations in our retail business, cloud services and the services we build. In this talk we will cover what generative AI is, its benefits and use cases. There are many questions we hear from our customers on how to best use AI in their business. In this talk we have identified 5 important considerations that customers can use to quickly build and deploy generative ai apps at scale. In addition, we will touch upon new services that customers can adopt to quickly choose from various LLMs available from leading startups, cost optimize their training and inference workload and increase developer productivity. The audience will leaving this talk knowing how to leverage AWS capabilities for their initiatives.

Building Generative AI Applications with SageMaker Foundational Models

Discover the power of AWS SageMaker Jumpstart Notebooks in this hands-on workshop. Learn to deploy foundational models for text & image generation, integrate them with Lambda, and create a front-end application. Explore how to harness AI for building creative, scalable, and user-friendly solutions. No prior AI/ML experience required. You will be provided AWS Accounts

AI/ML Development on AWS - What Model Should I Use?

We represent data ops, data engineering, and data science on a two-pizza data team at SmugMug. Within our company, a unique contribution by our team is an AWS AI/ML sandbox, based on SageMaker. We'd like to show you how we put that together, and how other teams at the company use it. Highlights are structured access to the data lake, policy-driven control of compute sizes, and GenAI-enhanced data catalogs so our colleagues quickly understand the data they're working with. We will demo how to use the sandbox to train, evaluate, and start the process of deploying models. With the rise of so many great third-party AI models, a critical use case has become rapid internal benchmarking of new models against our own data, so we can decide which one is the best fit for our application.

Sustainable machine learning for protecting natural resources

"Climate change, pollution, and illegal harvesting endanger the health of forests and oceans worldwide. AWS customers are addressing these challenges by using large sets of natural resource data and AWS artificial intelligence and machine learning (AI/ML) services at the edge. As data increases in complexity and scale, optimization is essential to reducing the environmental impact of ML processing while accelerating analysis, modeling, and simulation. In this session, learn how organizations use AI/ML to predict and respond to environmental threats, and take away practical measures you can deploy to reduce the carbon footprint of your models at all steps of the ML lifecycle."