Working with Azure Machine Learning compute

Azure Machine Learning provides a scalable cloud environment to build, train, and deploy ML models. It offers different computational targets for running experiments, training models, and serving predictions.

There are four targets overall, with two of these being managed internally from the workspace: compute instances and compute clusters. A compute instance is a managed VM that you use for development, training, and inferencing needs. It’s essentially a dedicated, personal workstation in the workspace. It can be used to run Jupyter notebooks and scripts. A compute cluster is a managed scalable set of virtual machines that are used for the large-scale training of ML models. Compute clusters automatically scale up or down (within the limits you set) based on the workload. So, for example, you can declare a minimum and maximum node and the machine will scale based on demand, which helps us optimize costs. There is also external...