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, as well as writing outputs to locations such as an Azure SQL Database. For deployment, we will see the differences between batch and real-time 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.
You will learn:
- How to prepare, train, and evaluate models in code using Azure Machine Learning
- How batch pipelines differ from real-time inference pipelines
- How to register and version models in Azure Machine Learning