The topic for my talk at the Microsoft Build conference yesterday was “Migrating Existing Open Source Machine Learning to Azure”. The idea behind the talk was to show how you can take the open-source tools and workflows you already use for machine learning and data science, and easily transition them to the Azure cloud to take advantage of its capacity and scale. The theme for the talk was “no surprises”, and other than the Azure-specific elements I tried to stick to standard OSS tools rather than Microsoft-specific things, to make the process as familiar as possible.
In the talk I covered:
- Using Visual Studio Code as a cross-platform, open-source editor and interface to Azure services
- Using the Azure CLI to script the deployment, functions, and deletion of resources in the Azure cloud
- Using the range of data science and machine learning tools included in the Data Science Virtual Machine on Ubuntu Linux
- Using Python and Tensorflow to train a deep neural network on a GPU cluster with Azure Batch AI
- Running sparklyr with RSTudio on a Spark cluster using aztk
It's not quite the same without the demos (and there's no recording available just yet), but you may find the notes and references in the slides below useful.