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
Managing Petabyte-Scale Datasets
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- The Google File System, Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung, 2003 Proceedings of the nineteenth ACM symposium on Operating systems principles DOI: 10.1145/945445.945451 - Describes the design and implementation of the Google File System, a foundational distributed file system architecture that influenced many modern petabyte-scale storage solutions like HDFS.
- WebDataset: A High-Performance I/O Format for Large-scale Deep Learning, Kyle K. Kayastha, Brant C. Faircloth, Andreas K. Foerster, Benjamin S. Glick, Brian K. Stewart, Jan Schlüter, 2021 arXiv preprint arXiv:2106.01429 - Details WebDataset, an efficient data format and loading library designed for streaming large-scale deep learning datasets directly from object storage, addressing optimized file formats and data streaming.
- Data Management for Machine Learning: A Survey, Jens Dittrich, Stefanie Scherzinger, Felix Naumann, Kai-Uwe Sattler, Volker Markl, 2022 ACM Computing Surveys, Vol. 55 (ACM) DOI: 10.1145/3547192 - Provides a comprehensive overview of data management challenges and solutions in machine learning, covering aspects from data ingestion and storage to feature engineering and metadata, highly relevant to managing large datasets for MLOps.