Mani Khanuja

Intelligent agents with memory capabilities can deliver smarter, more personalized interactions—but implementing memory in production is no easy task. In this session, we’ll cover how to build context-aware agents using Amazon Bedrock AgentCore Memory.

As organizations seek to harness their proprietary data while maintaining security and compliance, Amazon Bedrock provides a comprehensive framework for building tailored AI applications. Using Amazon Bedrock Knowledge Bases and Amazon Bedrock Data Automation, organizations can create AI solutions that truly understand their unique business context, terminology, and requirements.

Amazon Bedrock offers a managed Retrieval Augmented Generation (RAG) capability, connecting foundation models to your data. This session explores the latest techniques to improve response accuracy and optimize costs in Amazon Bedrock Knowledge Bases.

Foundation models such as Claude are commonly used to create new pieces of original content, such as short stories, essays, social media posts, and webpage copy, and also to summarize text from articles, blog posts, books, and more.

Your generative AI applications can deliver better responses by incorporating organization-specific data through a technique known as Retrieval Augmented Generation (RAG).

Amazon SageMaker provides comprehensive tools and libraries for building ML models. In this chalk talk, learn how to launch SageMaker notebooks in a few clicks and install the latest open-source Jupyter extensions.

The field of natural language processing (NLP) is developing rapidly, and NLP models are growing increasingly large and complex. Through strong ecosystem partnerships with organizations like Hugging Face and advanced distributed training capabilities, Amazon SageMaker is one of the easiest platforms with which to quickly train NLP models.