Jupyter Notebooks: If you're already using Jupyter Notebooks, the SDK has some extras that you should install. This article also provides additional usage tips for the following tools:
Additional cost incurred for Azure Databricks. Overkill for experimental machine learning, or smaller-scale experiments and workflows. Ideal for running large-scale intensive machine learning workflows on the scalable Apache Spark platform. Additional cost incurred for Linux VM (VM can be stopped when not in use to avoid charges). Lack of control over your development environment and dependencies. The entire SDK is already installed in your workspace VM, and notebook tutorials are pre-cloned and ready to run. Easy to scale and combine with other custom tools and workflows.Ī slower getting started experience compared to the cloud-based compute instance.Įasiest way to get started. 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. Necessary SDK packages must be installed, and an environment must also be installed if you don't already have one. Run with any build tool, environment, or IDE of your choice. Environmentįull control of your development environment and dependencies.
The following table shows each development environment covered in this article, along with pros and cons.
#Install jupyter notebook in virtualenv how to
Learn how to configure a Python development environment for Azure Machine Learning.
0 kommentar(er)
