Version control of azure machine learning workspace notebooks

Question

I'm trying to work with the capacities of the new Azure ML Workspace and I can't find any option to track my notebooks on git.

It's this possible as well as you can do with Azure notebooks? If not is possible... how it's suposed to work with this notebooks? Only inside this workspace?

Thanks!

Answer 1

There is an entire concept around this called ML Ops . There are also plenty of sample notebooks around this, how to for example use Azure ML together with Azure DevOps. Eg here .

Answer 2

AFAIK, Git isn't currently supported by Azure Machine Learning Notebooks. If you're looking for a more fully-featured development environment, I suggest setting one up locally. There's more work up front, but it will give you the ability to version control. Check out this development environment set-up guide. https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-environment

| Environment                                                   | Pros                                                                                                                                                                                                                                    | Cons                                                                                                                                                                                 |
|---------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Cloud-based Azure Machine Learning compute instance (preview) | Easiest way to get started. The entire SDK is already installed in your workspace VM, and notebook tutorials are pre-cloned and ready to run.                                                                                           | Lack of control over your development environment and dependencies. Additional cost incurred for Linux VM (VM can be stopped when not in use to avoid charges). See pricing details. |
| Local environment                                             | Full control of your development environment and dependencies. Run with any build tool, environment, or IDE of your choice.                                                                                                             | Takes longer to get started. Necessary SDK packages must be installed, and an environment must also be installed if you don't already have one.                                      |
| Azure Databricks                                              | Ideal for running large-scale intensive machine learning workflows on the scalable Apache Spark platform.                                                                                                                               | Overkill for experimental machine learning, or smaller-scale experiments and workflows. Additional cost incurred for Azure Databricks. See pricing details.                          |
| The Data Science Virtual Machine (DSVM)                       | Similar to the cloud-based compute instance (Python and the SDK are pre-installed), but with additional popular data science and machine learning tools pre-installed. Easy to scale and combine with other custom tools and workflows. | A slower getting started experience compared to the cloud-based compute instance.                                                                                                    |