Summary

In this chapter, you explored various ways in which to use the machine learning models that you have been training in this book. You can make either real-time inferences or batch process a large number of records in a cost-effective manner. You started by registering the model you would use for inferences. From there, you can either deploy a real-time endpoint in ACI for testing or in AKS for production workloads that require high availability and automatic scaling. You explored how to profile your model to determine the recommended container size to host the real-time endpoint. Following this, you discovered Application Insights, which allows you to monitor production endpoints and identify potential production issues. Through Application Insights, you noticed that the real-time endpoint you produced wasn't exposing a swagger.json file that was needed by third-party applications, such as Power BI, to automatically consume your endpoint. You modified the scoring function...