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LLM Engineer's Handbook

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4.9

By Paul Iusztin
October 2024 | 522 pages

Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning

Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production

Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications

Understanding the LLM Twin Concept and Architecture

Understanding the LLM Twin Concept and Architecture Understanding the LLM Twin concept Planning the MVP of the LLM Twin product Building ML systems with feature/training/inference pipelines Designing the system architecture of the LLM Twin Summary References

Tooling and Installation

Tooling and Installation Python ecosystem and project installation MLOps and LLMOps tooling Databases for storing unstructured and vector data Preparing for AWS Summary References

Data Engineering

Data Engineering Designing the LLM Twin’s data collection pipeline Gathering raw data into the data warehouse Summary References

RAG Feature Pipeline

RAG Feature Pipeline Understanding RAG An overview of advanced RAG Exploring the LLM Twin’s RAG feature pipeline architecture Implementing the LLM Twin’s RAG feature pipeline Summary References

Supervised Fine-Tuning

Supervised Fine-Tuning Creating an instruction dataset Creating our own instruction dataset Exploring SFT and its techniques Fine-tuning in practice Summary References

Fine-Tuning with Preference Alignment

Fine-Tuning with Preference Alignment Understanding preference datasets Creating our own preference dataset Preference alignment Implementing DPO Summary References

Evaluating LLMs

Evaluating LLMs Model evaluation RAG evaluation Evaluating TwinLlama-3.1-8B Summary References

Inference Optimization

Inference Optimization Model optimization strategies Model parallelism Model quantization Summary References

RAG Inference Pipeline

RAG Inference Pipeline Understanding the LLM Twin’s RAG inference pipeline Exploring the LLM Twin’s advanced RAG techniques Implementing the LLM Twin’s RAG inference pipeline Summary References

Inference Pipeline Deployment

Inference Pipeline Deployment Criteria for choosing deployment types Understanding inference deployment types Monolithic versus microservices architecture in model serving Exploring the LLM Twin’s inference pipeline deployment strategy Deploying the LLM Twin service Autoscaling capabilities to handle spikes in usage Summary References

MLOps and LLMOps

MLOps and LLMOps The path to LLMOps: Understanding its roots in DevOps and MLOps Deploying the LLM Twin’s pipelines to the cloud Adding LLMOps to the LLM Twin Summary References

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