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
Data Preprocessing Pipelines for LLMs
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- Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing, Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Haoyuan Li, Scott Shenker, Ion Stoica, 2012 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI '12) (USENIX Association) DOI: 10.5555/2393325.2393341 - This foundational paper introduces Resilient Distributed Datasets (RDDs), the core abstraction behind Apache Spark, which is vital for scalable and fault-tolerant distributed data processing.
- SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing, Taku Kudo, John Richardson, 2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (Association for Computational Linguistics) DOI: 10.18653/v1/D18-2012 - This paper details the SentencePiece library, which implements subword tokenization algorithms like BPE and WordPiece, widely used in LLM preprocessing.