The Complete ML Project Workflow: Development to Production

Many courses teach model training, but few explain how to turn a notebook into a reliable system. This course introduces the full project lifecycle required to deliver real-world business value using machine learning. By the end of this course, you will understand the essential stages of an ML project, from initial data exploration and model development to deployment, maintenance, and monitoring, enabling you to transition from isolated experiments to production-ready solutions. What you'll learn: * Understand the critical steps for translating a business need into a solvable machine learning problem. * Practice data preparation and feature engineering techniques essential for robust model training. * Build foundational ML models and evaluate their performance using standard metrics. * Configure basic experiment tracking and model versioning for reproducibility and team collaboration. * Apply core concepts of containerization and deployment patterns suitable for production environments. * Implement basic monitoring and maintenance strategies to ensure model reliability over time. We begin with key terminology and foundational concepts, then systematically walk through the development, training, validation, and operationalization phases of an ML project, focusing on practical written exercises. This course is designed for absolute beginners in machine learning who want to understand the practical workflow of a Data Scientist or ML Engineer. No prior experience with MLOps or production systems is required. Start learning how to deliver machine learning projects that work in the real world today.