MLOps: Deploying and Monitoring Models End-to-End

Many data science courses stop after model training, but the real challenge is deploying and maintaining that model in a live environment. Machine Learning Operations (MLOps) provides the structure necessary to manage the complexity of production ML systems. This practical, text-based course guides you through the complete lifecycle of a production ML service. You will learn foundational concepts like containerization and pipeline automation, and apply industry-standard tools for model versioning, experiment tracking, and continuous monitoring, allowing you to build reliable and scalable AI solutions. What you'll learn: Understand the core components of the MLOps lifecycle, from data ingestion to automated retraining. Configure containerized environments using Docker for consistent and reproducible model deployment via API services. Apply version control techniques (DVC, MLflow) to track models, data, and experiments effectively. Automate model training and deployment workflows using CI/CD principles and pipeline orchestration tools like Airflow. Implement continuous monitoring strategies to detect performance drift, data quality issues, and trigger alerts. Practice designing a resilient retraining loop to ensure the deployed model maintains accuracy over time. The course begins with essential MLOps terminology and system architecture, then progresses through practical implementation steps for building and deploying a complete end-to-end service. We cover setting up infrastructure, managing dependencies, and establishing continuous feedback loops. This course is designed for data scientists, aspiring ML engineers, and developers who have basic Python knowledge and want to learn how to operationalize machine learning models. No prior experience with MLOps tools or production deployment is required. Start your journey toward becoming a skilled Machine Learning Engineer today.