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Workload Manager enhances SAP monitoring on Google Cloud by providing unified, system-centric observability that correlates application and infrastructure metrics for improved performance and reliability.

Explore the latest enhancements and features in Google Data Cloud, including improvements in AlloyDB Omni, BigQuery vector search, Datastream integrations, Cloud SQL disaster recovery, Conversational Analytics, and serverless Spark within BigQuery to empower enterprise data analytics, AI, and database management.

Leverage the Strands Agents SDK and Tavily's secure web intelligence APIs to rapidly build flexible, enterprise-grade AI research agents powered by advanced large language models integrated via Amazon Bedrock.

Explore the Amazon Strands Agents SDK, an open-source framework offering model-driven AI agent architectures, multi-agent collaboration, robust AWS integration, and built-in observability for scalable and secure production deployments.

Leveraging custom plugins with Amazon Q Business to unify structured and unstructured data for enhanced support analytics using Retrieval-Augmented Generation and precise querying techniques.

Learn how to leverage Amazon Nova foundation models and constrained decoding to generate reliable structured outputs for AI applications using tool configurations and schema definitions.

AWS Batch integration with Amazon SageMaker Training jobs enables intelligent job scheduling, automated resource management, and efficient utilization of costly GPU instances to optimize machine learning training workflows.

Modular and SF Compute have launched a Large Scale Inference Batch API that combines a real-time GPU spot market with a high-performance inference stack to dramatically reduce AI inference costs and optimize resource utilization across heterogeneous hardware platforms.

Databricks announces the launch of its Vancouver Development Center to leverage local data and AI talent for advancing strategic innovations.

Insights on building and maintaining a long-term competitive edge in AI technology.

MIT researchers developed a provably efficient algorithm that leverages algebra and geometry to enable machine learning models to effectively and computationally efficiently handle symmetric data, improving accuracy and reducing training data requirements.

Leveraging AI-powered test failure triage and semantic search techniques, Salesforce reduced developer bottlenecks by 30%, accelerating test failure resolution with trusted, context-driven automation at scale.