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
Challenges in Serving Large Models
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- vLLM: Efficient LLM Serving with PagedAttention, Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lichao Yu, Daniel Friedman, Xin Jin, Joseph E. Gonzalez, 2023 Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (NeurIPS) DOI: 10.48550/arXiv.2309.06180 - Introduces PagedAttention, a memory optimization technique critical for managing the KV cache in LLM serving, and presents vLLM, an efficient serving system.
- A Survey of Large Language Model Acceleration, Lianmin Zheng, Ying Sheng, Hanwen Chang, Wei-Ming Chen, Xiangru Lian, Zhaoyang Zhang, Zhiqiang Xie, Puxin Xu, Yiyuan Dong, Renjie Liu, Xingyao Chen, Hao Zhang, Kaiwen Zhang, Zhuohan Li, Zixuan Wu, Siyuan Zhuang, Joseph Gonzalez, Yi Wu, Michael Mahoney, Archita Sharma, Fan Lai, Yinghui Li, Junjie Liu, Chris Van Durme, Guangxuan Song, Shangguang Wang, Wen-mei Hwu, Yonghong Yan, Zhi Yang, Zhenglian Wu, Yuandong Tian, Zhiruo Wang, Haotian Tang, Hantian Ding, Michael Jordan, Dawn Song, Michael I Jordan, 2024 arXiv preprint DOI: 10.48550/arXiv.2312.15166 - Offers a comprehensive review of techniques for accelerating large language models, addressing memory optimization, computational efficiency, and system design, pertinent to serving challenges.
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers, Elias Frantar, Shuming Ma, Saleh Ashkboos, Oleg Rybakov, Torsten Hoefler, 2023 Proceedings of the 40th International Conference on Machine Learning (ICML 2023) DOI: 10.48550/arXiv.2210.17323 - Presents a post-training quantization method tailored for large generative models, significantly reducing memory footprint and computational cost for inference.
- Large Language Model Inference: The Cost of Waiting, Quentin Lhoest, Lysandre Neis, 2023 (Hugging Face Blog) - Examines the challenges of LLM inference, focusing on the trade-offs between latency and throughput, and clarifies concepts like continuous batching and efficient KV cache management.