فهرست مطالب

• Cover
• Copyright
• Contributors
• Table of Contents
• Preface
• Chapter 1: Understanding the LLM Twin Concept and Its 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
• Chapter 2: Tooling and Installation
• Python ecosystem and project installation
• MLOps and LLMOps tooling
• Databases for storing unstructured and vector data
• Preparing for AWS
• Summary
• References
• Chapter 3: Data Engineering
• Designing the LLM Twin’s data collection pipeline
• Gathering raw data into the data warehouse
• Summary
• References
• Chapter 4: 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
• Chapter 5: Supervised Fine-Tuning
• Creating an instruction dataset
• Creating our own instruction dataset
• Exploring SFT and its techniques
• Fine-tuning in practice
• Summary
• References
• Chapter 6: Fine-Tuning with Preference Alignment
• Understanding preference datasets
• Creating our own preference dataset
• Preference alignment
• Implementing DPO
• Summary
• References
• Chapter 7: Evaluating LLMs
• Model evaluation
• RAG evaluation
• Evaluating TwinLlama-3.1-8B
• Summary
• References
• Chapter 8: Inference Optimization
• Model optimization strategies
• Model parallelism
• Model quantization
• Summary
• References
• Chapter 9: 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
• Chapter 10: 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
• Chapter 11: 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
• Appendix: MLOps Principles
• Packt Page
• Other Books You May Enjoy
• Index