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Total results: 10000
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Chalk talk
AIM394-NEW300 – AdvancedTransforming multimodal content with Amazon Bedrock Data Automation
Amazon Bedrock Data Automation is a new gen AI–powered capability that transforms unstructured content from documents, images, video, and audio into structured data at scale. It serves use cases ranging from insurance claims processing to media asset management, advertising, and compliance. Amazon Bedrock Data Automation enables simple customization of outputs to generate specific insights in formats compatible with existing systems. Developers can configure Amazon Bedrock Data Automation via the Amazon Bedrock console using sample data and then integrate its unified multimodal inference API into their applications for high-accuracy, consistent processing. Join this chalk talk to learn how Amazon Bedrock Data Automation can help you transform your unstructured content.re:Invent6 - Dec -
Chalk talk
AIM350-NEW300 – AdvancedSupercharge your generative AI applications with Amazon Nova models
Join this chalk talk to explore how to best optimize the usage of Amazon Nova models for application-specific use cases. Learn about the fine-tuning and distillation capabilities of the models and connecting the models to agent frameworks. Also explore how to utilize their function calling capabilities. Finally, discover how to use Retrieval Augmented Generation (RAG) systems to leverage the models' understanding capabilities.re:Invent6 - Dec -
Chalk talk
STG353-NEW300 – AdvancedAccelerate data discovery with Amazon S3 Metadata
Amazon S3 Metadata is a new feature that accelerates data discovery in Amazon S3 with near real-time object metadata that is stored in fully managed tables optimized for analytic workloads. As objects are added and removed, Amazon S3 Metadata automatically refreshes the metadata to reflect the latest changes. With Amazon S3 Metadata, you can easily generate, store, and query the metadata for your Amazon S3 objects, helping you quickly prepare data for business analytics, real-time inference applications, and more. Join this chalk talk to learn how to get started with Amazon S3 Metadata.re:Invent6 - Dec -
Chalk talk
AIM392-NEW300 – AdvancedResponsible AI development and deployment
In this chalk talk, learn about the AWS approach to responsible AI (RAI) and forward-looking responsible scaling policies. Hear how AWS defines RAI requirements from first principles (that is, our policies) in collaboration with experts. Learn how AWS instills RAI in our systems from the start, and learn about the AWS approach to the next evolution of policies as the models scale.re:Invent6 - Dec -
Chalk talk
STG370-NEW300 – AdvancedSimplify data management with Amazon S3 Tables
Managing and querying data at scale requires building and maintaining external systems to support transactions, table maintenance, and performance optimization, which can be costly and complex. Amazon S3 Tables is purpose-built to store tabular data in Apache Iceberg tables. With Amazon S3 Tables, you can create, store, and manage tables in just a few steps, and query your data with existing AWS Analytics and third-party tools. In this chalk talk, learn how to get started and benefit from automated performance optimizations, simplifying data management as you grow.re:Invent6 - Dec -
Chalk talk
ANT355-NEW300 – AdvancedAccelerate generative AI app development with Amazon Bedrock IDE
Join this chalk talk to uncover how Amazon Bedrock IDE within Amazon SageMaker Unified Studio empowers users to rapidly build, customize, and verify generative AI applications through governed collaboration. This streamlined experience facilitates generative AI application development with advanced capabilities like agent and flow creation, Retrieval Augmented Generation (RAG), prompt engineering, and guardrails. It provides rapid iteration and optimization for tailored applications. Discover how Amazon Bedrock IDE democratizes generative AI development, fosters cross-functional collaboration, and unlocks its full potential.re:Invent6 - Dec -
Chalk talk
AIM369300 – AdvancedOptimize cost performance using Amazon SageMaker inference
Amazon SageMaker’s inference optimization toolkit helps reduce how long it takes to optimize foundation models and achieve the best price performance for your use case. You can choose from a menu of optimization techniques, apply them to your models, validate performance improvements, and deploy the models in a few clicks. Employing techniques like speculative decoding, quantization, and compilation, SageMaker inference delivers up to two times higher throughput while reducing costs by up to 50% for models like Llama 3, Mistral, and Mixtral. It significantly reduces engineering costs by eliminating the need for research, experimentation, and benchmarking predeployment resources for developers.re:Invent5 - Dec