Summary
In this chapter, we continued refactoring the Age Calculator example that we started in Chapter 6, Automating the Machine Learning Process Using AWS Step Functions, to further streamline the overall ML automation process, using AWS Step Functions.
Not only have we seen how ML practitioner teams can tighten their integration with the development (or platform) teams by providing the entire ML workflow as a CI/CD pipeline artifact, but we also saw how—when combined with the codified artifacts created in Chapter 6, Automating the Machine Learning Process Using AWS Step Functions—each team can focus on their specific area of expertise. Now, the development teams don't have to upskill their understanding of how the ML process works to adapt the CI/CD pipeline to accommodate the ML process. Alternatively, the ML practitioner team can contribute their expertise to the pipeline development, instead of simply providing a trained ML model and expecting the other...
