How to build an Azure ML Pipeline

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With machine learning becoming more and more an engineering problem the need to track, work together and easily deploy ML experiments with integrated CI/CD tooling is becoming more relevant than ever.

In this session we take a deep-dive into the DevOps process that comes with Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy and manage models. We zoom into how the data science process can be made traceable and deploy the model with Azure DevOps to a Kubernetes cluster.

At the end of this session you have a good grasp of the technological building blocks of Azure machine learning services and can bring a machine learning project safely into production.

About our speaker:
Henk is a Cloud Advocate specializing in Artificial intelligence and Azure with a background in application development. He is currently part of the regional cloud advocate team in the Netherlands. Before joining Microsoft, he was a Microsoft AI MVP and worked as a software developer and architect building lots of AI powered platforms on Azure.

He loves to share his knowledge about topics such as DevOps, Azure and Artificial Intelligence by providing training courses and he is a regular speaker at user groups and international conferences.