Automated Machine Learning on AWS

Chapter 8: Automating the Machine Learning Process Using Apache Airflow

When building an ML model, there is a fundamental principle that all ML practitioners are aware of; namely, an ML model is only as robust as the data on which it was trained. In the previous four chapters, we have primarily focused on automating the ML process using a source code-centric mechanism. In other words, we applied a DevOps methodology of Continuous Integration and Continuous Deployment to automate the ML process by supplying the model source code, tuning parameters, and the ML workflow source code. Any changes to these artifacts would trigger a release change process of the CI/CD pipeline.

However, we also supplied static abalone data, downloaded from the UCI ...