Streamlining data monitoring with whylogs and MLflow

Streamlining data monitoring with whylogs and MLflow. MLflow is an open source framework that unifies model lifecycle management, used by Facebook, Zillow, Microsoft, and others. It works with any ML library, framework, or language, removing barriers to rapid prototyping and quicker turnaround times for solving business problems in production. whylogs is an open source, lightweight, high performance statistical data logging library that enables a fire-and-forget approach to logging data quality by profiling the data during training and as it flows through the model once it has been deployed. By integrating whylogs into the MLflow runtime, we can add data quality monitoring to the model pipeline. This allows engineers and data scientists to catch data quality issues during training as well as detect data drift after deployment, enabling a more informed analysis of the model's performance over time.

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