ML Flow Integration
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Fiddler allows your team to onboard, monitor, explain, and analyze your models developed with .
This guide shows you how to ingest the model metadata and artifacts stored in your MLflow model registry and use them to set up model observability in the Fiddler Platform:
Exporting Model Metadata from MLflow to Fiddler
Uploading Model Artifacts to Fiddler for XAI
Refer to this of the Databricks integration guide for onboarding your model to Fiddler using model information from MLflow.
Using the you can query the model registry and get the model signature which describes the inputs and outputs as a dictionary.
Sharing your helps Fiddler explain your models. By leveraging the MLflow API you can download these model files:
Finally, you can upload all the model artifacts to Fiddler:
Once you have the model file, you can create a file in this model directory that describes how to access this model.
Alternatively, you can skip uploading your model and use Fiddler to generate a to get low-fidelity explanations for your model.
Please refer to the guide for detailed information on model artifacts, packages, and surrogate models.