MLflow is an open source framework created by Databricks to simplify model lifecycle management. It handles model tracking and deployment, and helps with interoperability between different ML tools.
One of the key features of MLflow is the ability to track metrics both during the training process and once the model is deployed. By integrating whylogs into the MLflow runtime, you can log data quality metrics as part of the model's pipeline:
After enabling the integration, whylogs can be used to profile the data flowing through the pipeline when running MLflow jobs:
Once whylogs profiles have been generated, they are stored by MLflow along with all the other artifacts from the run. They can be retrieved from the MLflow backend and explored further:
For additional information and in-depth examples, check out our sample notebook 🙂