ML operations in Azure
You successfully registered a trained model, an environment, a scoring file, and an inference configuration in the previous section. You optimized your model for scoring and deployed it to a managed Kubernetes cluster. You autogenerated client SDKs for your ML services. So, can you finally lean back and enjoy the success of your hard work? Well, not yet! First, we need to make sure that we have all our monitoring in place so that we can observe and react to anything happening to our deployment.
First, the good points: with Azure Machine Learning deployments and managed compute targets, you will get many things included out of the box with either Azure, Azure Machine Learning, or your service used as a compute target. Tools such as the Azure Dashboard on the Azure Portal, Azure Monitor, and Azure Log Analytics make it easy to centralize log and debug information. Once your data is available through Log Analytics, it can be queried, analyzed, visualized, alerted...