Henk Boelman

Take a deep look into Azure Machine Learning, a cloud service that helps you build, train, deploy, and manage models. Walk through the data science process and then have some fun creating a ML recognition model based on the Simpsons cartoon with PyTorch. You'll leave this session with a better grasp of the technological components of Azure Machine Learning services.

Intelligent experiences powered by AI can seem like magic to users. Developing them, however, is cumbersome involving a series of sequential and interconnected decisions along the way that are pretty time consuming. What if there was an automated service that identifies the best machine learning pipelines for a given problem/data? Automated machine learning does exactly that! Learn how to quickly and efficiently build high accuracy ml models for classification, regression and forecasting scenarios using code and code-free pathways.

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.