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
Techniques for Hallucination Detection
Was this section helpful?
- Survey of Hallucination in Large Language Models, Ziqi Ji, Nayeon Lee, Pang Wei Koh, Shangyu Tong, Raghuveer Thirukovalluru, Chauncey Wang, Faisal Radwan, Sohee Park, Sana Malik, Xiang Ren, H. Andrew Schwartz, 2023 ACM Computing Surveys (CSUR), Vol. 56 (Association for Computing Machinery (ACM)) DOI: 10.1145/3613694 - Comprehensive overview of hallucination causes, detection, and mitigation strategies in LLMs.
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, Yarin Gal and Zoubin Ghahramani, 2016 Proceedings of the 33rd International Conference on Machine Learning (ICML), Vol. 48 (PMLR) DOI: 10.48550/arXiv.1506.02142 - Introduces Monte Carlo Dropout as a practical method for quantifying uncertainty in deep learning models, directly relevant to the uncertainty quantification section.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela, 2020 Advances in Neural Information Processing Systems, Vol. 33 (Curran Associates, Inc.) DOI: 10.48550/arXiv.2005.11401 - Presents Retrieval-Augmented Generation (RAG), a fundamental approach for grounding LLMs with external knowledge, which is a core technique for external verification.
- Measuring and Improving Factual Consistency Using Natural Language Inference, Sean Welleck, Samira Paranjape, Jason Baldridge, Yejin Choi, 2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (Association for Computational Linguistics) DOI: 10.18653/v1/D19-1406 - Discusses how Natural Language Inference (NLI) can be used to measure and improve the factual consistency of generated text, directly applicable to evidence comparison.