What is MLOps? - Introduction to Machine Learning Operations with MLflow

Learn the fundamentals of MLOps (Machine Learning Operations) through this beginner-friendly 13-minute video tutorial that breaks down complex concepts into simple, understandable terms. Discover why maintaining machine learning models in production is expensive and challenging, and explore how MLOps solves critical issues like model drift and data drift. Understand the difference between model drift (when model performance degrades over time) and data drift (when input data characteristics change), and learn why these problems make ML model maintenance a nightmare without proper operations practices. Explore how MLOps bridges the gap between fast, cheap model deployment and sustainable production maintenance by automating pipelines, versioning models, and ensuring continuous availability - essentially applying DevOps principles to machine learning systems. Get introduced to MLflow, an essential tool for production ML systems, and follow along with hands-on demonstrations that show how to track experiments, compare hyperparameters across multiple runs, evaluate different algorithms including linear regression, random forests, and gradient boosting, and register models for production deployment. Work through four practical tasks: tracking your first experiment, hyperparameter tuning, model comparison, and model registry setup for production use. Access free MLOps labs for hands-on practice and gain industry best practices for building and maintaining production machine learning systems, making this tutorial perfect for ML engineers, data scientists, DevOps engineers, and anyone working with production ML workflows.