Azure Machine Learning offers a low-code approach to performing machine learning tasks, but what happens when you need to do something a little more complex? In this talk, we will go beyond the Azure Machine Learning designer and show how to use Azure Machine Learning in a code-first environment. We will understand how the Pipeline metaphor works within code and use that to generate and deploy models, using the example of a code-first model performing batch inference. We will also review how Azure Machine Learning handles model registration and versioning. This talk assumes some basic familiarity with Azure Machine Learning and the Python language.
I presented this session for the Triangle Area SQL Server Users Group's Business Intelligence and Data Science special interest group on 2024-01-23. The video is available on YouTube.
I have created a playlist for Azure Machine Learning YouTube videos. These include most of the content of this talk series, as well as some additional notes.
Click here to access the slides for this presentation.
The slides are licensed under Creative Commons Attribution-ShareAlike.
Click here to access demo code for this presentation.
The source code is licensed under the terms offered by the GPL.