Machine Learning In Production: Azure, Docker, Kubernetes

Pick up a book on Machine learning and it will explain the process for machine learning, many citing CRISP-DM as the ideal process. CRISP-DM is an iterative approach to Data Mining. It starts with business understanding the flows to data understanding, data preparation, modelling, evaluation, then either loops back around or your model is deployed. How it is deployed, well no one ever tells you that! It is this problem this course solves. If you look at the image to the right you will see the vast array of options available. With each comes more complexity and sophistication.

In this session we will build a basic model and promote them into production, using a variety of techniques in Azure. The biggest problem a lot of customers face is not how to build a machine learning model, but it which technology they should use. This question depends on so much and as a result there are multiple options in Azure. This session will demonstrate the various options for deploying a model. We will end the day with an example which will allow you to deploy and model, in any language using Docker, Python and Kubernetes.

Speakers

Wednesday 30 September 2020