Summary

In this chapter, you learned how you can define AzureML pipelines using the AzureML SDK. These pipelines allow you to orchestrate various steps in a repeatable manner. You started by defining a training pipeline consisting of two steps. You then learned how to trigger the pipeline and how to troubleshoot potential code issues. Then you published the pipeline to register it within the AzureML workspace and acquire an HTTP endpoint that third-party software systems could use to trigger pipeline executions. In the last section, you learned how to schedule the recurrence of a published pipeline.

In the next chapter, you will learn how to operationalize the models you have been training so far in the book. Within that context, you will use the knowledge you acquired in this chapter to author batch inference pipelines, something that you can publish and trigger with HTTP or have it scheduled, as you learned in this chapter.