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Discover how Hinge approaches model serving as a long-term ownership challenge, focusing on creating sustainable AI platforms by addressing the "long tail" of model ownership to empower teams to innovate without operational burden.
Discover Okta's journey from traditional data processing to embedded OLAP systems, exploring the shift to mini serverless databases and how databases are re-entering application engineering domains.
Explore the observability challenges in distributed ML training with Ray as Nikita Vemuri shares insights from scaling across thousands of nodes and designing effective monitoring systems that balance data visibility with privacy.
Explore the architecture of SlateDB, an embedded storage engine built on object storage, learning how to implement features like conditional writes, checkpoints, and remote compaction for efficient stream processing and durable execution.
Explore how compound AI systems that combine LLMs with data and tools create intelligence rivaling human capabilities, offering insights for building AI workflows and general-purpose agents that outperform humans.
Discover how to build an LLM-powered analytics bot for Slack that enhances data operations through SQL query generation, chat summarization, and account lookups using AWS services at Twitch.
Explore Apple's Private Cloud Compute technology and how it delivers device-level privacy to remote servers, offering cryptographically-provable privacy guarantees for sensitive data and AI applications at scale.
Discover practical strategies for scaling RAG and GraphRAG systems to handle hundreds of millions of documents, with insights on orchestration, monitoring, and open standards for production-ready AI knowledge systems.
Discover insights on building domain-specific AI agents for legal work, balancing AI with human interaction, leveraging expertise, and maximizing quality in production environments.
Discover how LinkedIn's viral year-in-review generator scaled to 300,000 users, processing trillions of tokens in two weeks. Learn to build resilient multi-model AI workflows, optimize costs, and implement production-ready patterns for model orchestratio…
Discover Microsoft's AutoGen framework for building AI agent systems that transform complex workflows, accelerate timelines, boost accuracy, and reduce costs through automation.
Explore how to build reliable agentic AI systems from stochastic components, drawing inspiration from robotics, cybernetics, and biology to develop intuitions for non-deterministic decision making systems.
Explore how errors compound in multi-turn AI systems and learn to use "eval agents" for trajectory evaluation to improve agent performance through real-world failure examples and practical assessment techniques.
Explore comprehensive risk management frameworks for generative AI applications, covering reliability assessment approaches for RAG-augmented chatbots and autonomous agents to minimize failure modes in real-world deployments.
Explore the future of Retrieval-Augmented Generation with Tengyu Ma as he discusses advancements in embedding models and rerankers that create more robust, scalable solutions beyond current heuristic-heavy approaches.
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