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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

MLOps for Large Models (LLMOps)

Prerequisites: Strong foundation in machine learning concepts, practical experience with standard MLOps principles and tools, proficiency in Python, and familiarity with cloud platforms (AWS, Azure, or GCP). Experience with containerization (Docker, Kubernetes) is beneficial.

Level: Advanced

What You'll Learn

LLM Infrastructure Design
Architect scalable infrastructure for training and serving large language models, considering GPU/TPU resources and networking.
Distributed Training Management
Implement and manage distributed training jobs for multi-billion parameter models using frameworks like DeepSpeed or Megatron-LM.
Efficient Fine-tuning Operations
Operationalize parameter-efficient fine-tuning (PEFT) techniques within MLOps workflows.
Advanced LLM Deployment
Deploy large models using optimized inference servers, quantization, and specialized serving patterns.
LLM Monitoring and Observability
Implement comprehensive monitoring strategies for LLM performance, cost, drift, and output quality.
Cost Optimization
Apply strategies to manage and optimize the significant costs associated with training and serving large models.
RAG System Operations
Manage the operational aspects of Retrieval-Augmented Generation systems, including vector database management.

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