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Discover how Amazon Q Developer can help you manage and troubleshoot workloads faster with hands-on demos from AWS experts Jay Busch and Rajakumar Sampathkumar.
Dive into implementing Knowledge Bases with Amazon Bedrock Agents, exploring Retrieval-Augmented Generation models and enhancing AI reasoning through external information sources.
Dive into common ECS issues, failure modes, and best practices with a live troubleshooting session featuring an AWS TAM guest who helps solve application problems.
Dive into AWS threat hunting fundamentals with practical CloudTrail and GuardDuty techniques to transform reactive security into proactive detection strategies for teams of all experience levels.
Discover how the Valkey engine in ElastiCache and MemoryDB improves performance and reduces costs, with demonstrations on implementation, migration, and integration.
Discover how to leverage OpenSearch Service's vector database capabilities to power generative AI applications, modernize search experiences, and simplify AI deployments through Amazon Bedrock integration.
Discover flexible approaches to meet GenAI needs with AWS, exploring Amazon Bedrock for serverless inferencing and SageMaker AI for serverful inferencing with greater infrastructure control.
Discover how Amazon SageMaker Unified Studio enables teams to securely discover, prepare, and collaborate on data assets while building analytics and gen AI applications through a single experience.
Explore Amazon S3 Tables architecture and benefits for managing tabular data at scale with Apache Iceberg support, improved performance, simplified security, and automatic cost management across various AWS query engines.
Discover how unified governance can transform your data and AI systems with practical steps for breaking down silos, improving data quality, and creating seamless operations that deliver faster, more reliable outcomes.
Discover how Amazon Data Firehose simplifies database change capture and replication to Apache Iceberg Tables in S3, enabling real-time streaming from PostgreSQL and MySQL sources for faster analytics with minimal performance impact.
Discover how Amazon SageMaker Lakehouse unifies data across multiple sources, enables querying through Apache Iceberg compatible tools, and secures data with fine-grained access controls.
Discover how AWS Storage building blocks integrate with AI/ML services to optimize training time and costs for machine learning and large language models, enabling faster innovation across industries.
Discover how Amazon S3 Metadata creates a queryable layer for efficient data discovery and management. Learn to explore objects using custom metadata tags, transforming how you handle data lakes and optimize discovery processes.
Discover how AWS empowers customers with innovative storage, analytics, and AI solutions, focusing on Amazon S3 and SageMaker to create a unified data foundation with minimal operational burden.
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