Summary
Automating ML solutions in an end-to-end fashion is no easy task and if you've made it this far, feel proud. Most modern data science organizations can easily train models. Very few can implement reliable, automated, end-to-end solutions as you have done in this chapter.
You should now feel confident in your ability to design end-to-end AutoML solutions. You can train models with AutoML and create ML pipelines to score data and retrain models. You can easily ingest data into Azure and transfer it out of Azure with ADF. Furthermore, you can tie everything together and create ADF pipelines that seamlessly ingest data, score data, train data, and push results to wherever you'd like. You can now create end-to-end ML solutions.
Chapter 11, Implementing a Real-Time Scoring Solution, will cement your ML knowledge by teaching you how to score data in real time using Azure Kubernetes Service within AMLS. Adding real-time scoring to your batch-scoring skillset will make...
