Workshop: Machine Learning – From model to production using the cloud, containers and DevOps

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 is deployed.

How it is deployed, well no one ever tells you that! Well, I want to talk about it!

In this full-day session we will build a series of basic models and promote them into production. This will be an interactive session, so make sure you have your laptop with you. As we go through the day we will talk about the following:

• Developing a Machine Learning Engineering environment
• Develop multiple basic machine learning models
• Deploy multiple basic machine learning models
• Develop an architecture capable of supporting and deploying any machine learning language

Sounds awesome right? My intention is to show you a method for deploying machine learning models.

We will do this by looking at the following tech stack:

• Microsoft Azure – A Cloud environment to deploy to. (All the tech we are using will work on a platform of your choice)
• Python – To build our models
• Docker – A container to run our models
• Kubernetes – A container runtime environment to handle the load balancing of our models.
• Azure Service Bus – A stream service for our models
• PowerBI – A reporting tool to visualise the usage of our models.

We will use a composition of other languages as we go. All the scripts we will use will be available on GitHub for you to follow along.

At the end of the day we will have built a simple model and deployed it. You will take away a tried and tested architecture for deploying a model in to production. I will demo a method for deploying changes to your model using DevOps.