nfmoore/azure-databricks-containers-mlops-example-scenarios
Prescriptive guidance for building, deploying, and monitoring machine learning models with Azure Databricks using containers in line with MLOps principles and practices.
This project offers clear guidance for deploying and managing machine learning models that predict outcomes in real-time. It takes a trained model from Azure Databricks and shows how to package it into a container, then deploy it as a web service for immediate use. This is for machine learning engineers and MLOps professionals who need to put models into production efficiently.
Use this if you are an MLOps professional or ML engineer looking for a prescriptive guide to deploy Azure Databricks models into production as online inference services using containers.
Not ideal if you are new to machine learning or not working with Azure Databricks and other Azure services for model deployment.
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25
Forks
9
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 02, 2026
Commits (30d)
0
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