As machine learning (ML) becomes ubiquitous in technology, there is an increasing need for well-engineered ML systems and processes that enable ML algorithms to drive business value. Enterprise ML has experienced a shift in focus from just the ML models themselves to the software engineering, infrastructure and best practices necessary to support ML at scale in production. Bringing a model from a data scientist’s notebook to running live in an application requires robust systems, MLOps and ML governance.
This course is an introduction to ML systems in production that will demonstrate and give students exposure to how real production ML systems operate. Using Python, Docker, Kubernetes, Google Cloud and various open-source tools, students will bring the different components of an ML system to life and setup real, automated infrastructure. It will be mostly in Python, docker, kuberentes, and google cloud in addition to lots of open source tools.
It is expected you have familiarity with an object-oriented programming language (preferably Python) and experience with basic machine learning concepts and models. Some previous exposure to a cloud environment (AWS, Google Cloud, Azure, etc…) or other software engineering experience would be helpful but not necessary.
Certificates are awarded at the end of the program at the satisfactory completion of the course. Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.