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Explore how Chain-of-Draft prompting on Amazon Bedrock significantly reduces token usage and latency compared to Chain-of-Thought, optimizing large language model reasoning for cost-effective, high-performance AI applications.

DrP is Meta’s scalable root cause analysis platform that automates incident investigations using a flexible SDK, ML algorithms, and seamless workflow integrations to significantly reduce MTTR and improve on-call productivity across large-scale systems.

Explore three hands-on labs demonstrating how to deploy AI agents on Google Cloud using Vertex AI Agent Engine, Cloud Run, and Google Kubernetes Engine for scalable, secure, and production-ready applications.

Stochastic rounding, a probabilistic rounding technique supported by modern hardware like Google Cloud TPUs and NVIDIA Blackwell GPUs, enables stable and efficient low-precision training for large generative AI models by preserving small gradient updates.

Explore how Google Kubernetes Engine enhances agentic workloads with flexible ADK integration, secure Agent Sandbox isolation using gVisor, and accelerated Pod Snapshots for near-instant sandbox restoration.

A comprehensive 2025 review of cloud security fundamentals and the integration of evolving AI technologies to enhance defense, threat intelligence, and trusted cloud services.

Looker's latest updates introduce self-service Explores for local data analysis, tabbed dashboards for organized storytelling, and custom themes to align analytics with corporate branding.

A comprehensive recap of Google Cloud's groundbreaking AI innovations, infrastructure advancements, security initiatives, and customer successes throughout 2025.

Cloudflare's 2025 transparency report highlights innovative technical and collaborative approaches to combat streaming abuse and unauthorized content distribution while balancing transparency, user rights, and regulatory compliance.

Cloudflare outlines its Code Orange: Fail Small resilience plan focusing on controlled rollouts, improved failure handling, and optimized emergency access to prevent future network outages.

BED-LLM leverages Bayesian Experimental Design to enable Large Language Models to adaptively and intelligently gather information through multi-turn interactions by maximizing expected information gain.

SOCI indexing in Amazon SageMaker Studio significantly reduces container startup times by enabling lazy loading of container images, enhancing productivity for AI/ML workloads.