This course, part of the Machine Learning Engineering for Production (MLOps) Specialization, provides a comprehensive guide for building production-ready machine learning systems. Starting with an introduction to MLOps concepts, the course explores the complete ML lifecycle from model development to deployment and monitoring. Participants learn how to implement best practices for scalable, reliable, and maintainable ML pipelines. Topics include setting up development environments, versioning datasets and models, testing models, and deploying them in production. The course emphasizes practical exercises with real-world examples, teaching how to monitor performance, detect model drift, and retrain models effectively. Learners gain hands-on experience in managing end-to-end ML workflows, understanding the integration of CI/CD pipelines, and applying operational best practices for machine learning. By the end of this course, participants will have the skills to design, build, and maintain robust ML systems capable of handling real-world production requirements. This course is ideal for data scientists, ML engineers, and AI practitioners seeking to operationalize machine learning efficiently and at scale.