The issue with machine learning pipelines is that they need to pass state from one step to another. When this works, it's a beautiful thing to behold. When it doesn't, well, it's not pretty, and I think the clip below sums this up pretty well. Azure ML Pipelines are no stranger to this need for passing data between steps, so you have a variety of options at your disposal. This means it's not always easy to find the best one, and I've often seen people confused when trying to pick the best option.

It's been a while since we've last posted about this, but we're excited to present new capabilities we've added to the VS Code Azure Machine Learning (AML) extension. We're guessing many of you may be reading about Azure ML and the extension for the first time – don't worry, we're here to explain! Azure ML is a machine learning service that provides a wide set of tools and resources for data scientists to build, train, and deploy models. The AML extension is a companion tool to the service which provides a guided experience to help create and manage resources from directly within VS Code. The extension aims to streamline tasks such as running experiments, creating compute targets, and managing environments, without requiring the context-switch from the editor to the browser.