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
Tooling Considerations for LLMOps
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- ZeRO: Memory Optimizations Toward Training Trillion-Parameter Models, Samyam Rajbhandari, Cong Guo, Jeff Rasley, Shaden Smith, Yuxiong He, 2020 SC '20: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (ACM (Association for Computing Machinery)) DOI: 10.1145/3418856.3418915 - Describes ZeRO, a memory optimization technique used in DeepSpeed for training large models, important for LLM scalability.
- The Ray Project Documentation, Anyscale, Inc., 2024 - Official documentation for Ray, a distributed computing system used for LLM data processing (Ray Data), distributed training (Ray Train), and serving (Ray Serve).
- vLLM: Efficient Memory Management for Large Language Model Serving, Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, Ion Stoica, 2023 arXiv preprint arXiv:2309.06180 DOI: 10.48550/arXiv.2309.06180 - Presents vLLM, a high-throughput and low-latency LLM inference engine that uses PagedAttention, addressing deployment needs.
- LLMOps: Towards a Standardized, Streamlined, and Scalable Workflow for Large Language Models, Zhifeng Zhang, Bo Qiao, Mengjie Li, Guohao Li, Yuan Jiang, Chengyue Wu, Weiqi Wang, Long Cheng, Huaijun Li, Bo Dong, Xiaoyong Hu, 2023 arXiv preprint arXiv:2306.05942 - A survey paper that outlines the lifecycle and considerations of LLMOps, offering a conceptual framework for tooling choices.