With recent developments in the AI space, workflows for deploying, monitoring, and maintaining ML models have changed. In this course, Keith McCormick—an independent data miner, trainer, speaker, and author—breaks down the phases of an ML project and guides you through model evaluation, scoring, deployment, and model maintenance. Learn about data engineering and MLOps in the ML lifecycle, as well as the basics of ML modeling. Get a useful deployment checklist that you can use in model evaluation. Find out how to score traditional ML models, a “black box” model, and an ensemble. Go over batch and real-time scoring. Plus, explore model monitoring and the best frequency for model rebuilding.
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