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
Infrastructure Requirements for Large Models
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- ZeRO: Memory Optimizations Toward Training Trillion-Parameter Models, Samyam Rajbhandari, Cong Guo, Erin Grant, Zhun Liu, Hao Ma, Aakanksha Chowdhery, Yuxiong He, 2020 Advances in Neural Information Processing Systems 33 (NeurIPS 2020), Vol. 33 (NeurIPS) DOI: 10.5591/978-1-7138-0457-3.NeurIPS.2020.00762 - This paper introduces ZeRO, a memory optimization technique that partitions optimizer states, gradients, and parameters across devices to enable training of large deep learning models efficiently.
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism, Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro, 2019 arXiv preprint arXiv:1909.08053 DOI: 10.48550/arXiv.1909.08053 - Describes how to train very large language models by effectively using model parallelism, including tensor and pipeline parallelism, across multiple GPUs.
- A Cloud-Scale Architecture for Accelerating AI, Norman P. Jouppi, Zhifeng Chen, David Pattison, Carla J. del Rosario, Nils Hogstrom, David A. Huffman, Daniel F. Kramer, Andrew B. New, Christopher B. Perry, Evan E. Radecki, Timothy B. Smith, Ryan M. Some, David R. Turnbull, Andrew J. Veit, John W. Weigelt, Dean M. Wilkes, Cliff C. Young, Yifeng Chao, Andrew L. Chien, Patrick K. H. Chiang, John A. Gunnels, Mark R. O'Connor, Anant N. Agarwal, Jeffrey Dean, Paul N. Hilfinger, Jeffrey S. Riegel, Edward A. Sacks, Manjot Singh, Stephen W. Smith, Jonathan S. Taylor, David L. Wells, Kenneth C. Yocum, 2021 Proceedings of the 48th Annual International Symposium on Computer Architecture (ISCA '21) (ACM) DOI: 10.1145/3465033.3467614 - Presents the architecture of Google's Tensor Processing Units (TPUs) designed for cloud-scale AI workloads, detailing their specialized interconnect and processing capabilities.
- NVIDIA H100 Tensor Core GPU Architecture, NVIDIA Corporation, 2022 (NVIDIA) - An official whitepaper detailing the NVIDIA H100 GPU architecture, including innovations like the Transformer Engine and NVLink, which are relevant for large language model computation.