Change is constant in the Machine Learning world, so monitoring production models is crucial to prevent wrong predictions caused by data drift. Learn how to build a dashboard for data drift detection in Python using the Evidently package and Jupyter Notebook, and deploy it to a web application using the Mercury framework. Data drift comes in different types, including target drift and covariate drift, and can be detected through statistical tests or Machine Learning models. After detecting data drift, updating the ML model can be done in various ways, depending on the data. Start building a robust ML system today by following this articles step-by-step guide.
