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Antje works with AWS customers, helping them understand the benefits and capabilities of the AWS platform and advising them on best practices for developing and deploying cloud-based applications. Barth is also a popular speaker, author, and trainer, focusing on big data, analytics, and machine learning. Antje has authored multiple publications related to these topics, and she frequently presents at conferences and workshops.

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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

Protect your workloads against generative AI cyberthreats

Generative AI and natural language processing (NLP) AI models have been made available to the general public with ease of use and high levels of accuracy. While there are a myriad of potential benefits and benign uses for the technology, there are also many concerns about it’s use to develop malicious exploits and more effective cyberattacks. What does that mean for cybersecurity? And how can you defend against the misuse of Gen AI capabilities? In this workshop attendees will learn and build a working defense with AWS security services to combat the potential misuse of sophisticated Gen AI bot capabilities. .

Bear Necessities: Edge AI for Wildlife Conservation

Join us as we explore the exciting world of wildlife conservation through the lens of machine learning. Discover how the BearID Project is using advanced technology to monitor brown bears in their natural habitats. From training models on Amazon SageMaker to deploying them on edge devices using AWS IoT Greengrass and Arm's software defined camera framework, this talk promises to be a wild ride!

Cost Optimization Strategies in AWS

This intro into cost optimization at AWS walks through the required change in mindset for working on the cloud. This talk focuses on the five pillars of cost optimization which is right-sizing, elasticity, pricing model, storage and mechanisms for cost optimization.

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.

Fast, Scalable, and Efficient Kubernetes

We've all had to make trade-offs to implement a solution. But with modern Kubernetes a lot of the limitations of yesteryear have been overcome with new features and implementations. Justin will show you how to run Kubernetes clusters that can scale effortlessly, be highly utilized, and react fast to new requirements.

Leveraging Cost Categories to build AWS Chargeback reports

In this presentation, I will walk through Dolby's FinOps journey and how we migrated from using a third party reseller for our AWS Billing to bringing everything in house by leveraging AWS Cost Categories to build a simple cost allocation and chargeback solution for our organization. I will talk through the reasoning for the change, how we set it up, and what the benefits were for our company.

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."

Building scalable end-to-end deep learning pipelines in the cloud.

Machine and deep learning became essential for a lot of companies for internal and external use. One of the main issues with its deployment is finding the right way to train and operationalize the model within the company. Serverless approach for deep learning provides simple, scalable, affordable and reliable architecture for it. My presentation will show how to do so within AWS infrastructure. Serverless architecture changes the rules of the game - instead of thinking about cluster management, scalability, and queue processing, you can now focus entirely on training the model. The downside within this approach is that you have to keep in mind certain limitations and how to organize training and deployment of your model in a right fashion. My presentation will show how to utilize services like AWS Batch, AWS Fargate, Amazon SageMaker, AWS Lambda, and AWS Step Functions to organize scalable deep learning pipelines."

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

Running Apache Spark on Amazon Web Services (AWS) offers numerous advantages for processing large-scale data workloads. However, achieving optimal performance, cost-efficiency, and reliability requires careful planning and implementation. In this talk, we will delve into the ultimate guideline for running Spark on AWS in a maintainable, reliable, and cost-effective manner, with a focus on integrating AWS Glue and SageMaker. We will start by discussing the fundamental concepts of Spark, AWS Glue, and SageMaker, providing a solid foundation for understanding their integration. AWS Glue is a fully managed extract, transform, and load (ETL) service that simplifies data preparation tasks, while SageMaker provides a powerful platform for building, training, and deploying machine learning models. This will be a complete demo driven session, wherein we will spend entire time on the console/code.

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

Live streaming is massively popular due to its interactive and unpredictable nature. But, the unpredictability means that sometimes less than desirable events can be live streamed and that offensive, insensitive chat messages are posted in chat messages during a live stream. Manual admin intervention can be used to prevent or stop offensive live streams and moderate inappropriate chat messages, but manual intervention isn't always 100% perfect. Automated moderation can help, but lacks the contextual awareness of the manual approach. The best approach is a healthy mix of manual and automatic moderation. In this session, we'll look at the various approaches to moderating a live stream and how they can be approved with the help of AI and ML. We'll look at how to analyze portions of a live stream for offensive or inappropriate content and use that analysis as a prompt for moderator intervention. We'll also look at how analyze chat messages for sentiment analysis and PII detection.

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

In this session, attendees will learn practical strategies to leverage the high-performance capabilities of AWS’s Arm-based Graviton2 processors for AI workloads. From understanding the underlying architecture to applying specific software optimizations, the session aims to provide actionable insights for developers and data scientists seeking to improve their AI workload performance on AWS.