MLOps Fundamentals

No, prior knowledge of machine learning and Python is assumed if you start this track.

This track does not include any programming language. It covers theories such as CI/CD and CM/CT, experiment tracking, model registries, feature stores, and different deployment strategies.

This track is suitable for data scientists and machine learning engineers who wish to expand their knowledge into the world of production machine learning software. It will also benefit data engineers, software engineers, DevOps engineers, and software architects.

This track will help equip you with an MLOps mindset and will help you to gain an understanding of the key elements of operating machine learning applications in production. Additionally, it will help you acquire in-depth knowledge on topics such as data drift and model drift.

This track generally takes 14 hours to complete, as it consists of several courses.

Skill tracks focus on developing knowledge in a particular domain, while career tracks offer an end-to-end curriculum to kick start and progress in a particular domain. Skill tracks are more general and are suited for those who wish to acquire a solid foundation in the domain, while career tracks are more focused and are suited for those who wish to progress towards a certain career path.

This track covers topics such as MLOps concepts, deployment and life cycling of MLOps, fully automated MLOps, and developing machine learning models for production.

Yes, the track is structured to provide you with practical experience as much as theoretical knowledge. You will get to work on real-world examples to learn and explore the different concepts related to MLOps.