Build and deploy PyTorch models with Azure ML

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 then ever. In this session we take a deep-dive into Azure Machine Learning, a cloud service that you can use to track as you build, train, deploy, and manage models. We use the Azure Machine Learning Python SDK to manage the complete life cycle of a PyTorch model, from managing the data, to train the model and finally run it into a production Kubernetes cluster. At the end of this session you have a good grasp of the technological building blocks of Azure machine learning services and train a PyTorch model on scale.

About Henk Boelman

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.