LLMOps with MLflow: Fine-Tuning and Inference Guide

Managing the lifecycle of large language models requires structured workflows for tracking experiments, fine-tuning, and reliable deployment. This text-based guide teaches you how to implement practical LLMOps using MLflow to manage language models effectively. You will read through detailed conceptual breakdowns and code-focused explanations, learning how to configure MLflow tracking, fine-tune models for text classification tasks, log parameters and metrics, and serve models for inference. What you'll learn: - Understand core LLMOps principles and foundational MLflow terminology - Track experiments, prompts, and evaluation metrics using MLflow Tracking - Fine-tune language models for custom document classification workflows - Register and version models within the MLflow Model Registry - Deploy and serve fine-tuned models for production inference - Apply evaluation strategies and best practices to monitor model performance The course starts with essential terminology and the fundamentals of the LLMOps workflow before guiding you step-by-step through dataset preparation, fine-tuning, and model management. This course is designed for aspiring machine learning engineers, data scientists, and developers looking to structure their language model operations without requiring prior LLMOps experience. Start reading today to build and manage robust large language model pipelines with MLflow.