This MLOps course provides a complete guide for building and deploying production-grade machine learning projects. Participants learn the fundamentals of MLOps, including model deployment, pipeline creation, versioning, monitoring, and automation. The course emphasizes practical, hands-on learning, guiding learners through real-world scenarios to implement ML models efficiently and reliably. Topics include containerization with Docker, workflow orchestration with tools like Airflow, CI/CD integration, cloud deployment, and monitoring model performance in production environments. Additionally, learners explore best practices for reproducibility, scalability, and collaboration in ML projects. By combining theory with actionable projects, this course equips participants with the skills needed to take ML models from development to deployment while ensuring maintainability and robustness. By the end of the course, learners will be able to confidently implement MLOps pipelines, optimize model performance, and manage end-to-end machine learning workflows. This course is ideal for data scientists, ML engineers, and AI enthusiasts seeking to transition from building models to deploying them in production environments.