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LLM DEPLOYMENT & MLOps: Serving Large Language Models from Prototype to Production: A Practical Guide to FastAPI, Kubernetes, and Monitoring

Disponibilidad:
En stock

Peso con empaque:
0.57 kg

Devolución:
Sí

Condición
Nuevo

Producto de:
Amazon

Sobre este producto

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.

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