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Discover how Mercari leverages GenAI to enhance its C2C marketplace, from streamlining item listings to improving search relevance and product recommendations through advanced ML engineering techniques.
Explore advanced RAG techniques, from basic concepts to production-ready implementations, in this comprehensive guide to enhancing AI-powered information retrieval systems.
Master end-to-end LLM fine-tuning with W&B Models, from experiment tracking to model deployment in production. Learn to publish models, automate evaluations, and implement CI/CD workflows for efficient AI development.
Explore the latest advancements in agentic AI systems, from automated ML research to self-optimizing models, with insights into Sakana AI's groundbreaking developments and future prospects.
Enhance CI/CD pipelines with W&B Registry for efficient model management, evaluation, and deployment of top production models.
Master the development of AI agents with Weave platform, focusing on evaluation, monitoring, and deployment strategies for creating reliable and efficient business solutions.
Discover how Mercari scaled LLMOps for 23M+ users through prompt management, evaluation frameworks, and observability tools to accelerate development and enhance software quality.
Discover how Analog Devices tackles EV battery optimization using machine learning at the edge, covering data collection, experiment tracking, and overcoming AI limitations in automotive applications.
Explore multi-agent systems evolution with CrewAI's CEO, covering enterprise implementation strategies, maturity levels, and real-world production use cases.
Discover advanced inference optimization techniques for enterprise AI, including sliding window attention, speculative decoding, and guided decoding to balance speed, cost, and performance.
Explore how GenAI transforms research-to-product cycles, covering evaluation strategies, agentic systems, and Google's infrastructure for scalable AI deployment.
Explore PyTorch's evolution as the open language of AI, covering its ecosystem, research adoption, and future vision for planet-scale training and inference.
Discover how to build top-performing programming agents through systematic experimentation loops, reinforcement learning techniques, and automated improvement processes for AI development.
Discover how NVIDIA builds scalable AI agents using data flywheels for enterprise deployments, covering architecture patterns, feedback loops, and continuous LLM improvement strategies.
Explore the emerging open source AI compute stack combining Kubernetes, Ray, PyTorch, and vLLM for scalable AI workloads with real-world case studies from Pinterest, Uber, and Roblox.
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