I am using this blog (https://databricks.com/blog/2020/10/13/using-mlops-with-mlflow-and-azure.html) to set-up MLOps using Azure Databricks & Azure ML. As mentioned in the blog, we deploy MLflow model into an Azure ML environment using the built in MLflow deployment capabilities, which is used for inference. A couple of questions -
1. How and where does the data prep come into picture before inference and how I can integrate that piece.
2. How to create a re-training workflow for the model?
Thanks.