In Azure Machine Learning one runs experiments to train or score a model, these experiments can be run separately or within a string of steps, called a pipeline. This pipeline can then be scheduled to run on either an event based schedule or time based.
Azure DevOps and Machine Learning are important tools for a data scientist. Learn about industry best practices for working with these tools in these posts.
Deploying a machine learning model to an Azure Container Instance (ACI) allows you to make live predictions against your trained model for testing or small production systems
Continuous integration and continuous deployment (CI/CD) pipelines in Azure DevOps are an effective way of automatically training your machine learning models after new code has been pushed to a branch
Having a project template repository in Azure DevOps to clone when starting new projects is helpful for data science teams who are wanting to speed up the creation of new solutions or help onboard new data scientists
Register your model in Azure Machine Learning along with additional files needed for scoring