Comprehensive Generative AI Engineering for LLMOps

Outline

Introduction

LLM Fundamentals for Ops

• Introduction to Generative AI and LLMs for Operations Workflows
• LLM Architecture and Deployment Considerations
• Implications of LLM architecture on deployment, scaling, and resource management

Prompt Engineering for Ops

• Introduction to Prompt Engineering
• Techniques for creating effective prompts
• Best practices for prompt design and optimization
• Developing prompts for IT and traditional Ops tasks
• Log analysis
• Alert generation
• Incident response
• Improving response to production outages and IT challenges with PE

LLM Integration for Ops

• Overview of key LLM APIs and libraries
• OpenAI API
• HuggingFace Transformers
• Strategies for integrating LLMs into monitoring, alerting, and automation tools
• Use Case Development
• Real-World Case Studies
• Building an LLM-powered monitoring and alerting system

Deployment and Management of Open-Source LLMs

• Introduction to Open-Source LLMs
• Advantages and limitations in production environments
• Best practices for deploying and managing open-source LLMs
• Techniques for managing LLM infrastructure, scaling, and performance
• Setting up Lllama 3 from HuggingFace

Containerization and Orchestration

• Containerizing LLM applications using Docker
• Orchestrating LLM containers using Kubernetes
• Deploying an LLM application using Docker and Kubernetes

Scaling LLM Applications

• Strategies for horizontal and vertical scaling
• Load balancing and auto-scaling techniques
• Implementing auto-scaling for an LLM application

Monitoring and Troubleshooting

• Key performance metrics for LLM applications
• Automated Testing for LLMOps
• Differences of LLMOps testing and traditional software testing
• Evaluation using CI/CD Tools
• Evaluating LLM problems like hallucinations, data drift, unethical/harmful outputs
• Monitoring tools and techniques (e.g., Weights and Biases, CircleCI)
• Setting up monitoring for an LLM application
• Creating dashboards and alerts for key metrics

Security, Compliance, and Cost Optimization

• Securing LLM application infrastructure and data
• Ensuring compliance with relevant regulations and standards
• Strategies for optimizing resource usage and costs in cloud-based LLM deployments

Conclusion