Development Environment

Lifecycle stages:

Introduction

Onboarding

Supported User Workflows

Infrastructure & Compute

Development Environment

Experimentation

Training

Deployment

Monitoring and Alerts

Overview

AI Platform uses the managed Azure ML infrastructure to automatically provision integrated development environments (IDEs) for both JupyterLab and VS Code notebooks.

These environments are built on Conda and come pre-configured with essential libraries and Azure ML integrations—allowing you to develop, train, and deploy models seamlessly.

Compute is handled by a dedicated Azure Kubernetes Service (AKS) cluster, while a shared persistent data volume ensures that notebooks, environments, and data can be retained across sessions and accessible across projects. This setup supports scalable, collaborative, and reproducible AI/ML workflows on the platform.

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Documents available for this stage

📄️ How to Set Up a Notebook

Automatically generate a web-based IDE for notebooks.

📄️ How to Locally Set Up Web-Based Notebooks

Use local VS Code to connect to an AI Platform pod and open notebooks.

📄️ How to Run the Example Notebook

Run and test the 'example-notebook.ipynb'.

📄️ How to Create a New Environment

Create an environment that you can later load as a kernel for your notebook.