When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
First, this course explores managing the experimentation process using MLflow with a focus on end-to-end reproducibility including data, model, and experiment tracking. Second, students...
Read MoreFirst, this course explores managing the experimentation process using MLflow with a focus on end-to-end reproducibility including data, model, and experiment tracking. Second, students operationalize their models by integrating with various downstream deployment tools including saving models to the MLflow model registry, managing artifacts and environments, and automating the testing of their models. Third, students implement batch, streaming, and real-time deployment options. Finally, additional production issues including continuous integration, continuous deployment are covered as well as monitoring and alerting. By the end of this course, you will have built an end-to-end pipeline to log, deploy, and monitor machine learning models. This course is taught entirely in Python.
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