Hello @MK RP ,
What models are available for serving
MLflow Model Serving is available for Python MLflow models. You must declare all model dependencies in the conda environment.
What is the model prediction endpoint
MLflow Model Serving allows you to host machine learning models from Model Registry as REST endpoints that are updated automatically based on the availability of model versions and their stages.
Each deployed model version is assigned one or several unique URIs. At minimum, each model version is assigned a URI constructed as follows:<databricks-instance>/model/<registered-model-name>/<model-version>/invocations
For example, to call version 1 of a model registered as iris-classifier, use this URI:https://<databricks-instance>/model/iris-classifier/1/invocations
You can also call a model version by its stage. For example, if version 1 is in the Production stage, it can also be scored using this URI:https://<databricks-instance>/model/iris-classifier/Production/invocations
What are the required inputs and their data types & What are the model outputs and data type
Regarding the input and output depends on the model which you are using. And most data can be categorized into 4 basic types from a Machine Learning perspective: numerical data, categorical data, time-series data, and text.
Is there any REST api for getting to this information?
Unfortunately, there's not an API for this today, our product team is working on this, and I will update this thread once it’s available.
Hope this will help. Please let us know if any further queries.
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