MLOps Fundamentals: Build and Automate Production ML Pipelines

Moving a machine learning model from a local notebook to a reliable production environment requires more than just good data science. It demands MLOps—the practices and tools that automate, monitor, and scale machine learning systems in the real world.\n\nIn this structured course, you will transition from basic model development to managing production-ready ML systems. You will understand how to bridge the gap between data science and operations, establishing robust workflows that keep models accurate and reliable over time.\n\nWhat you'll learn:\n- Understand the core terminology, principles, and stages of the MLOps lifecycle.\n- Track experiments, log parameters, and manage model versions using MLflow.\n- Package and serve machine learning models as production-ready APIs with BentoML.\n- Monitor model performance and system metrics using Grafana to detect drift.\n- Implement basic CI/CD pipelines and containerization concepts for automated deployment.\n- Apply best practices for data versioning and model governance in a collaborative environment.\n\nYou will start with foundational definitions and MLOps architecture before progressing to step-by-step written walkthroughs for model tracking, packaging, and monitoring. Through clear explanations and practical code-based exercises, you will build a solid conceptual and practical foundation.\n\nThis course is designed for aspiring ML engineers, data scientists, and DevOps professionals who are new to MLOps. No prior DevOps experience is required, though a basic understanding of Python and machine learning concepts is helpful.\n\nStart reading today to master the essentials of production machine learning systems.