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0.6. Resources

Is there additional project resources?

This course is supplemented with a variety of resources aimed at enriching your MLOps learning journey. A key resource is the MLOps Python Package, designed to exemplify how to structure an MLOps codebase efficiently. This package incorporates the dataset featured in the course, providing a holistic view of what your end project could resemble.

For those seeking to deepen their knowledge in specific areas, the course creators have also contributed insights through personal blog posts. These articles explore subjects like setting up Visual Studio Code for MLOps activities or employing Pydantic for robust data validation. Such resources offer additional insights and actionable advice to enhance your understanding and skills.

Can you suggest a new project resource?

The course adheres to an open-source ethos, warmly welcoming contributions that augment the educational value for all participants. Whether you've discovered an essential tool, library, or piece of literature that aligns with the course's aims, or you've developed your resources inspired by your coursework and are keen to share them, your contributions are greatly valued.

To propose a new project resource, please forward your suggestions to the course's repository and create an issue on GitHub. Your contributions not only aid your peers in their learning process but also play a crucial role in the continuous improvement and updating of the course materials to mirror the evolving landscape of MLOps best practices and innovations.

Additional vendor resources