This comprehensive MLOps tutorial series by DVCorg provides practical guidance for building, managing, and deploying machine learning projects efficiently. The course begins with an introduction to Continuous Integration (CI) for ML and best practices for handling large datasets that exceed Git’s capacity. Participants learn to track ML models using Git, GitHub Actions, and DVC, implementing automated workflows for reproducibility. Advanced lessons cover behavioral testing for models, integrating GPUs with GitHub Actions, and transforming Jupyter notebooks into reproducible DVC pipelines for end-to-end experiments. Learners also explore using DVC experiments in transfer learning, GitOps for ML, and best practices for building production-ready computer vision projects. Additional modules demonstrate ML experiment tracking with VS Code, Git, and DVC, helping participants manage, version, and monitor models effectively. By combining theory with hands-on tutorials, this course equips learners to implement CI/CD pipelines, automate ML workflows, and ensure robust, reproducible ML experiments. By the end of the series, participants will have the skills to operationalize machine learning efficiently, maintain version control, and build end-to-end pipelines for real-world projects. This course is ideal for data scientists, ML engineers, and AI practitioners seeking practical MLOps expertise.