MLOps with MLflow and Hugging Face Transformers

Managing machine learning experiments and deploying modern natural language processing models requires robust operational workflows. This text-based course guides you through foundational MLOps principles using MLflow and Hugging Face Transformers. You will learn how to organize experiment tracking, log parameters and metrics, manage model artifacts, and streamline the lifecycle of transformer-based architectures. What you'll learn: - Understand core MLOps terminology, experiment tracking lifecycles, and model management fundamentals - Configure MLflow to track parameters, training metrics, and evaluation results for transformer pipelines - Log and package Hugging Face models as reusable MLflow flavors and artifacts - Manage model versions and staging environments using the MLflow Model Registry - Apply basic evaluation tracking techniques for modern large language models and NLP tasks - Implement reproducible training workflows with clean Python code and environment definitions The course begins with foundational concepts and core definitions before progressing to structured, text-guided walkthroughs of model logging, registry workflows, and deployment preparation. Designed specifically for beginners, data practitioners, and aspiring machine learning engineers, no prior MLOps experience is required. Start reading today to build reliable, reproducible workflows for your transformer models.