Azure Machine Learning — Train, Deploy and Operate Models
How Azure Machine Learning really works: workspaces, compute clusters and serverless jobs, MLflow tracking, registries that promote models across workspaces, managed online endpoints with blue/green traffic, mirroring and autoscale, batch endpoints on dedicated or Spot clusters, the managed virtual network, and the quota, image-build and container errors that fail deployments.
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