September 13, 2024

Computer History Museum

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AWS Community Day

About AWS Community Day

AWS Community Day is a dynamic event celebrating the AWS community, uniting cloud enthusiasts, developers, and professionals from diverse fields. This event highlights the expansive universe of AWS technologies, offering participants the chance to dive into educational sessions, engage in practical workshops, and expand their professional networks.

Attendees will explore cutting-edge trends and practical applications of AWS services, fostering a collaborative environment rich in knowledge exchange and innovation. The event is designed to provide a platform for learning and sharing, with opportunities to gain insights from AWS experts and industry leaders.

Join us to connect with peers, enhance your AWS skills, and become part of a thriving community driven by shared learning and growth.

Topics at the AWS Community Day

Introduction to AWS Cloud Services
Building Scalable Applications on AWS
Enhancing Data Analytics with AWS
Cloud Security Best Practices
AI and Machine Learning Applications
Serverless Architecture on AWS
IoT Solutions using AWS
Cost Optimization Strategies
DevOps and Continuous Integration/Continuous Deployment (CI/CD) with AWS
Case Studies and Industry Insights

KEYNOTE SPEAKER

Principal Developer Advocate, AI & ML @ AWS.

Principal Developer Advocate, AI & ML @ AWS.

Antje Barth is a Principal Developer Advocate for generative AI at AWS. She is co-author of the O’Reilly books Generative AI on AWS and Data Science on AWS. Antje frequently speaks at AI/ML conferences, events, and meetups around the world. She also co-founded the Düsseldorf chapter of Women in Big Data.

DISTINGUISHED SPEAKERS

Transformative Technology Leader @ AWS

Arnab Sinha

Transformative Technology Leader @ AWS

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.

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Developer Relations @ AWS

Banjo Obayomi

Developer Relations @ AWS

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

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Lead Data Scientist at SmugMug + Flickr

Geoff Ryder

Lead Data Scientist at SmugMug + Flickr

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.

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Senior Solutions Architect @ AWS.

Isha Dua

Senior Solutions Architect @ AWS.

Optimizing GenAI Workloads on AWS

As GenAI models grow more complex, successfully optimizing their accuracy, cost efficiency, and deployment velocity on AWS becomes both increasingly critical and challenging. This session will provide an end-to-end guide for GenAI teams to maximize their workload efficiency on AWS. We’ll provide an overview of the full GenAI development lifecycle then do a deep dive into progressive model optimization techniques—from prompt engineering to RAG and fine tuning—exploring how each incrementally improves accuracy while minimizing retraining compute requirements. Shifting gears, we’ll share AWS best practices for optimizing GenAI model packaging, deployment, and inference using containers and hardware acceleration. Monitoring and maintaining production GenAI workloads presents its own challenges, which we’ll address through observability, data drift detection, and model degradation monitoring techniques and tools on AWS. Attendees will walk away with a clear framework for incrementally optimizing their GenAI workloads throughout the machine learning lifecycle with an eye toward maximizing performance while keeping costs in check.

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Solutions Architect - ML and Environmental Sustainability @ AWS.

Parth Patel

Solutions Architect - ML and Environmental Sustainability @ AWS.

Optimizing GenAI Workloads on AWS

As GenAI models grow more complex, successfully optimizing their accuracy, cost efficiency, and deployment velocity on AWS becomes both increasingly critical and challenging. This session will provide an end-to-end guide for GenAI teams to maximize their workload efficiency on AWS. We’ll provide an overview of the full GenAI development lifecycle then do a deep dive into progressive model optimization techniques—from prompt engineering to RAG and fine tuning—exploring how each incrementally improves accuracy while minimizing retraining compute requirements. Shifting gears, we’ll share AWS best practices for optimizing GenAI model packaging, deployment, and inference using containers and hardware acceleration. Monitoring and maintaining production GenAI workloads presents its own challenges, which we’ll address through observability, data drift detection, and model degradation monitoring techniques and tools on AWS. Attendees will walk away with a clear framework for incrementally optimizing their GenAI workloads throughout the machine learning lifecycle with an eye toward maximizing performance while keeping costs in check.

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AGENDA

TimeSession Details
Morning Sessions
08:00 AM - 4:00 PM
Badge pick up, Assisted Registration, Information Desk - Grand Lobby
08:30 AM - 09:30 AM
1 hour
Breakfast and Networking - Grand Hall
09:30 AM - 10:00 AM
30 minutes
Welcome, Introductions and Sponsors Parade - John Varghese, AWS Hero - Hahn Auditorium
10:00 AM - 10:45 AM
45 minutes
Keynote - Is this the final frontier? - Antje Barth - Principal Developer Advocate, AI & ML @ AWS - Hahn Auditorium
10:45 AM - 11:15 AM
30 minutes
Tea/coffee break and Networking - Grand Hall Sponsored by AWS
Tracks
Hahn Auditorium
Lovelace
Boole
11:15 AM - 11:55 AM
40 minutes
Workshop A - GenAI: Build a complete application using AWS Bedrock and Partyrock

Workshop B - Using Intel AI to accelerate your AI/ML workloads

--AWS and Intel

Creating Safer Online Communities by Using AI/ML for Live Stream Content Moderation

--Todd Sharp

Fast, Scalable, and Efficient Kubernetes

--Justin Garrison

12:00 noon - 12:25 PM
25 minutes
Sustainable machine learning for protecting natural resources

--Parth Patel, Isha Dua

Running Spark on AWS in maintainable, reliable and cost-effective way

--Suman Debnath

12:30 PM - 1:30 PM
1 hour
Lunch and Networking - Grand Hall SPONSORS WANTED!!
Post Lunch Sessions
Tracks
Hahn Auditorium
Lovelace
Boole
1:30 PM - 1:55 PM
25 minutes
Workshop A continued - GenAI: Build a complete application using AWS Bedrock and Partyrock


Workshop C - How to secure your APIs

--Traceable.AI or CheckPoint

AWS GameDay:


Building Generative AI Applications with SageMaker Foundational Models

--Banjo Obayomi

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

--Geoff Ryder

2:00 PM - 2:25 PM
25 minutes
Cost Optimization Strategies in AWS

--Franklin Aguinaldo

Maximizing AI Efficiency on AWS: A Practical Guide to Leveraging Arm’sArchitecture

--Tina Tsou

2:30 PM - 2:55 PM
25 minutes
Afternoon Tea break SPONSORS WANTED!!
Tracks
Hahn Auditorium
Lovelace
Boole
3:00 PM - 3:45 PM
45 minutes
Panel Discussion at the Hahn Auditorium
3:45 PM - 4:00 PM
Raffle & Closing Note - Hahn Auditorium

VOLUNTEERS

Shah

Aakash

Shah

Platinum Sponsors

AWS

AWS

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

Intel

Intel

Intel's innovation in AI is powering the smart and connected digital world we live in.

Gold Sponsors

Silver Sponsors

DNAnexus

DNAnexus

The world's most secure, trusted cloud platform and global network for scientific collaboration and accelerated discovery.


COMMUNITY PARTNERS

AWS Bay AreaBay Area InfracodersAdvanced AWSPublice Cloud SecurityAWS East Bay Official EventsAWS East Bay Official EventsData Science on AWSGenerative AI on AWS

Venue

Computer History Museum

1401 N Shoreline Blvd,

Mountain View, CA 94043