MLOps for Large Models (LLMOps)
Chapter 1: Foundations of LLMOps
Transitioning from MLOps to LLMOps
Unique Challenges of LLMs in Production
Infrastructure Requirements for Large Models
The LLMOps Lifecycle Stages
Tooling Considerations for LLMOps
Chapter 2: Infrastructure and Data Management at Scale
Designing Scalable Compute Infrastructure
Networking Considerations for Distributed Systems
Managing Petabyte-Scale Datasets
Data Preprocessing Pipelines for LLMs
Version Control for Large Data and Models
Cloud vs On-Premise Infrastructure Trade-offs
Practice: Setting up Scalable Storage
Chapter 3: Large Model Training and Fine-tuning Operations
Orchestrating Distributed Training Jobs
Implementing Data Parallelism Strategies
Implementing Model Parallelism Strategies
Utilizing Frameworks like DeepSpeed and Megatron-LM
Operationalizing Parameter-Efficient Fine-tuning (PEFT)
Experiment Tracking for Large-Scale Runs
Checkpointing and Fault Tolerance Mechanisms
Hands-on Practical: Distributed Training Setup
Chapter 4: LLM Deployment and Serving Optimization
Challenges in Serving Large Models
Model Packaging and Containerization for LLMs
GPU Inference Server Optimization
Implementing Model Quantization Techniques
Knowledge Distillation for Deployment
Advanced Deployment Patterns (Canary, A/B Testing)
Autoscaling Inference Endpoints
Serverless GPU Inference Considerations
Practice: Deploying a Quantized Model
Chapter 5: Monitoring, Observability, and Maintenance
Defining LLM-Specific Performance Metrics
Monitoring Infrastructure Utilization (GPU, Memory)
Tracking Operational Costs
Detecting Data and Concept Drift in LLMs
Monitoring LLM Output Quality (Toxicity, Bias)
Techniques for Hallucination Detection
Building Feedback Loops for Continuous Improvement
Logging and Observability Platforms for LLMOps
Hands-on Practical: Setting up Basic LLM Monitoring
Chapter 6: Advanced LLMOps Systems and Workflows
Operationalizing Prompt Engineering
Managing Retrieval-Augmented Generation (RAG) Systems
Vector Database Operations and Management
Automating LLM Retraining and Fine-tuning Pipelines
Security Considerations in LLMOps
Compliance and Governance in LLM Deployments
Integrating LLMOps with CI/CD Systems
Practice: Building a Prompt Management Workflow
Checkpointing and Fault Tolerance Mechanisms
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- DeepSpeed: Training large models with ease, speed and efficiency, Microsoft, 2024 (Microsoft) - Provides official documentation and tutorials on implementing efficient checkpointing and fault tolerance using the DeepSpeed framework, essential for large-scale distributed training of deep learning models.
- FSDP: Fully Sharded Data Parallel, Wei Feng, Will Constable, Yifan Mao, 2024 (PyTorch Foundation) - Offers official PyTorch guidance on using Fully Sharded Data Parallel (FSDP), including how to save and load sharded checkpoints, which is critical for scaling large models.
- Machine Learning Design Patterns, Valliappa Lakshmanan, Sara Robinson, and Michael Munn, 2020 (O'Reilly Media) - The book's 'Checkpointing' design pattern chapter provides an architectural perspective on implementing checkpointing for resilience and manageability in machine learning pipelines.
- SafeTensors: A more secure and faster way to store and load tensors, Hugging Face, 2024 - Explains the SafeTensors format, which addresses security and efficiency concerns in saving and loading large model states, offering a robust alternative to traditional serialization methods.