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Walk through getting the baseline model up to a proper implementation while gradually increasing the number of tracked objects.
Project looks to create a stand-alone MLflow model registry which sits on its own Azure Container Registry, using an image, connected to a blob storage (artifact store) and internal sqlite db (registry store).
A sample container of Mlflow/Python for deployment to AWS Lambda for ML model serving.