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.
Jupyter Notebook: https://github.com/hnky/AIDemo-DevOps-CatsAndDogs-AMLS