AFAIK, Git в настоящее время не поддерживается Azure ноутбуками с машинным обучением. Если вы ищете более полнофункциональную среду разработки, я предлагаю настроить ее локально. Впереди еще много работы, но она даст вам возможность контроля версий. Ознакомьтесь с этим руководством по настройке среды разработки. 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. |