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
Experiment Tracking for Large-Scale Runs
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- Weights & Biases Documentation: Tracking Large Language Models, Weights & Biases, Accessed 2025 (Weights & Biases) - Official documentation providing practical guidance and best practices for experiment tracking specifically tailored for large language models, covering metrics, artifacts, and system health.
- DeepSpeed: System Optimizations for Large-Scale Model Training, Samyam Rajbhandari, Cong Guo, Jeff Rasley, Shaden Smith, Yuxiong He, 2020 arXiv preprint arXiv:2008.01666 DOI: 10.48550/arXiv.2008.01666 - Introduces DeepSpeed, a distributed training framework whose optimizations (like ZeRO) require detailed tracking of distributed configurations and resource utilization as discussed in the section.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - A comprehensive book on MLOps system design, with chapters dedicated to experiment tracking, infrastructure, and managing the complexities of large-scale machine learning model development.
- Language Models are Few-Shot Learners, Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Anna Stooke, Erin Cooke, Scott Clark, Allie Schmidt, Aditya Ramesh, Andy Jones, Chris McMahon, Ambrose Slone, Chris Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei, 2020 Advances in Neural Information Processing Systems, Vol. 33 DOI: 10.55989/nips.2020.01633 - Details the architecture and training process of GPT-3, exemplifying the vast scale of LLM training and the importance of tracking extensive hyperparameters and configurations for such models.