This hands-on MLOps course teaches how to build and deploy production-ready machine learning projects using modern MLOps tools and best practices. Designed for aspiring machine learning engineers and data scientists, the course covers the complete machine learning lifecycle from data ingestion to deployment.

The course begins with a strong foundation in MLOps concepts, explaining how DevOps principles are applied to machine learning systems. You will then learn the fundamentals of ZenML, a powerful framework for creating reproducible and scalable ML pipelines.

Using a real-world customer satisfaction prediction project based on the Olist dataset, the course demonstrates how to design machine learning workflows from scratch. Topics include data preparation, cleaning, feature engineering, model development, evaluation, and pipeline orchestration.

A major focus is on creating structured pipelines using ZenML while integrating experiment tracking capabilities through MLflow. This allows teams to monitor experiments, compare model versions, and improve reproducibility.

The course also covers deployment pipelines, showing how trained models can be moved into production environments efficiently. Finally, you will build a Streamlit application that serves the machine learning model through an interactive user interface.

By the end of the course, you will have practical experience building an end-to-end MLOps project and understand how modern tools are used to create scalable, maintainable, and production-grade machine learning systems.