Large Language Models (LLMs) like OpenAI GPT,/Groovy Google Gemini, and Databricks DBRX are transforming industries, but effectively deploying and managing them requires more than traditional machine learning practices. LLMOps is a specialized set of techniques, tools, and workflows designed to tackle the unique challenges of working with LLMs in production. This presentation will explore what makes LLMOps distinct, why its essential, and how it enables organizations to harness the power of LLMs efficiently, at scale, and with reduced risks.

Introduction to LLMOps: Overview of its components from data preparation to deployment and monitoring
MLOps to LLMOps: Key differences including computational demands, fine-tuning with human feedback, and prompt engineering
Challenges & Solutions: Addressing LLM-specific issues like inference cost, model drift, and hallucination
Best Practices: Insights into data prep, governance, CI/CD pipelines, and model monitoring
Platform Tools: Exploring platforms like MLflow and Databricks for effective LLMOps implementation