SKU/Artículo: AMZ-B0G1H5HHLK
LLM DEPLOYMENT & MLOps: Serving Large Language Models from Prototype to Production: A Practical Guide to FastAPI, Kubernetes, and Monitoring
Format:
Hardcover
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.39 kg
0.39 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- LLM Deployment & MLOpsThe most dangerous phase in the LLM lifecycle isn't training. It's deployment.You’ve built a brilliant prototype in a notebook. It answers questions perfectly, but it costs too much and takes five seconds to reply. That's where 90% of Large Language Model (LLM) projects stall, the huge, complex leap from a functional demo to a reliable, low-latency, and cost-effective production service that can handle millions of requests.Traditional MLOps pipelines weren't built for the extreme demands of generative AI. You face a perfect storm of technical challenges: massive GPU memory requirements, variable inference latency due to high token counts, and the urgent need for real-time model monitoring to prevent performance drift. If your service slows down, the user experience breaks, costs skyrocket, and your entire project becomes a financial liability.This is the definitive, hands-on guide for MLOps engineers and AI architects dedicated to closing the production gap. You won’t just learn concepts; you’ll follow an end-to-end blueprint for building a resilient, fully automated deployment architecture designed specifically for the unique demands of modern LLMs.Stop Worrying About VRAM and Start Serving Millions of Tokens per Second.This book cuts through the complexity and provides clear, actionable strategies using the industry's most powerful tools. We dive deep into architectural patterns that guarantee high throughput and ironclad stability, ensuring your LLM is always available, fast, and economical.Inside, you will master the full deployment stack:Low-Latency API Design: Build high-speed, asynchronous LLM endpoints using FastAPI to minimize latency and maximize throughput, moving beyond basic REST APIs.Kubernetes Orchestration (K8s): Learn how to configure robust Kubernetes clusters, manage massive model weights, and implement advanced GPU scheduling and resource quotas.Scalability and Cost Control: Implement the Horizontal Pod Autoscaler (HPA) for dynamic scaling and learn the secrets of scaling to zero to eliminate idle cloud compute costs.High-Performance Serving: Maximize GPU utilization using specialized inference servers like vLLM and Triton, leveraging dynamic batching and PagedAttention to achieve state-of-the-art speeds.LLMOps Monitoring: Set up a complete observability stack using Prometheus and Grafana to track critical metrics like P99 latency, cost-per-query, and early detection of model drift.Safe CI/CD: Implement automated, zero-downtime deployment strategies, including Canary Releases and automated rollbacks, ensuring every model update is safe and reliable.The Production Blueprint for Enterprise AIThis book is more than theory; it's a battle-tested roadmap for achieving production-grade stability. If your job depends on moving Large Language Models out of the lab and into the hands of real users, reliably, cheaply, and quickly, you need this book.Click "Buy Now" and engineer the world-class inference infrastructure your business demands.
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AR$120.627
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AR$61.859
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