Tabla de Contenidos: “…Quantization with GPTQ and EXL2 -- Other quantization techniques -- Summary -- References -- Chapter 9: RAG Inference Pipeline -- Understanding the LLM twin's RAG inference pipeline -- Exploring the LLM twin's advanced RAG techniques -- Advanced RAG pre-retrieval optimizations: query expansion and self-querying -- Query expansion -- Self-querying -- Advanced RAG retrieval optimization: filtered vector search -- Advanced RAG post-retrieval optimization: reranking -- Implementing the LLM twin's RAG inference pipeline -- Implementing the retrieval module -- Bringing everything together into the RAG inference pipeline -- Summary -- References -- Chapter 10: Inference Pipeline Deployment -- Criteria for choosing deployment types -- Throughput and latency -- Data -- Understanding inference deployment types -- Online real-time inference -- Asynchronous inference -- Offline batch transform -- Monolithic versus microservices architecture in model serving -- Monolithic architecture -- Microservices architecture -- Choosing between monolithic and microservices architectures -- Exploring the LLM Twin's inference pipeline deployment strategy -- The training versus the inference pipeline -- Deploying the LLM Twin service -- Implementing the LLM microservice using AWS SageMaker -- What are Hugging Face's DLCs? -- Configuring SageMaker roles -- Deploying the LLM Twin model to AWS SageMaker -- Calling the AWS SageMaker Inference endpoint -- Building the business microservice using FastAPI -- Autoscaling capabilities to handle spikes in usage -- Registering a scalable target -- Creating a scalable policy -- Minimum and maximum scaling limits -- Cooldown period -- Summary -- References -- Chapter 11: MLOps and LLMOps -- The path to LLMOps: Understanding its roots in DevOps and MLOps -- DevOps -- The DevOps lifecycle -- The core DevOps concepts -- MLOps…”

Acceso restringido con credenciales, usuarios UPSA

Acceso restringido con credenciales, usuarios UPSA