This course builds upon and extends the DevOps methodology prevalent in software development to
build, train, and deploy machine learning (ML) models. The course is based on the four-level MLOPs
maturity framework. The course focuses on the first three levels, including the initial, repeatable, and
reliable levels. The course stresses the importance of data, model, and code to successful ML
deployments. It demonstrates the use of tools, automation, processes, and teamwork in addressing the
challenges associated with handoffs between data engineers, data scientists, software developers, and
operations. The course also discusses the use of tools and processes to monitor and take action when
the model prediction in production drifts from agreed-upon key performance indicators.
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