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
Unique Challenges of LLMs in Production
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- 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, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei, 2020 Advances in Neural Information Processing Systems, Vol. 33 (Curran Associates, Inc.) DOI: 10.48550/arXiv.2005.14165 - Introduces GPT-3, highlighting the immense scale of parameters that laid the groundwork for many of the operational challenges discussed.
- TensorRT-LLM Documentation, NVIDIA Corporation, 2025 (NVIDIA) - Official documentation for NVIDIA's high-performance inference library for large language models, providing insights into practical deployment and optimization.
- On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜, Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell, 2021 Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Association for Computing Machinery) DOI: 10.1145/3442188.3445922 - A critical paper discussing the ethical and societal risks of large language models, particularly concerning data quality, bias, and the potential for harmful outputs.