This MLOps Coding Course provides a hands-on introduction to implementing machine learning pipelines using Python and essential development tools. Participants start by setting up their system environment and exploring Python programming for ML workflows. The course covers creating projects with uv and uv projects, version control with Git, and collaborative development using GitHub. Learners also explore working with VS Code for efficient coding, managing Jupyter notebooks, and handling imports effectively. The course emphasizes building reproducible and maintainable pipelines, integrating coding best practices with MLOps principles. Practical exercises guide learners in creating end-to-end ML workflows, automating tasks, and ensuring version control across development stages. By the end of the course, participants will be able to code, manage, and deploy ML pipelines efficiently, understand MLOps principles in practice, and collaborate effectively on ML projects. This course is ideal for data scientists, ML engineers, and developers looking to enhance their MLOps coding skills and streamline ML project workflows.
