As more and more ML models are developed and deployed, the need arises to ensure that the models are effective and safe and that they perform as desired. Model monitoring, a core function of MLOps, helps data scientists and MLOps engineers to meet this need. In this course, data analytics expert Kumaran Ponnambalam discusses the types of monitoring needed for ML models. He deep dives into model drift monitoring and bias. For model drift, Kumaran goes over the types of drift monitoring and their causes. He explains different techniques for drift monitoring and how to execute them in python using open source libraries. For bias, Kumaran highlights various sources of bias and their impact. He also analyzes bias in python with open source libraries. Finally, he recommends some best practices for drift and bias monitoring.

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