Summary
In this chapter, we learned how to create an ML model. We learned that creating ML code is not that difficult and that the surrounding aspects are what make it complex. On top of that, we also learned about some basic terminologies such as AutoML, pre-built models, and MLOps.
As I mentioned in the introduction, ML is not a core skill that a Data engineer needs to have. But understanding this topic will give a data engineer a bigger picture of the whole data architecture. This way, you can imagine and make better decisions when designing your core data pipelines.
This chapter is the end of our big section on Building Data Solutions with GCP Components. Starting from Chapter 3, Building a Data Warehouse in BigQuery, to Chapter 8, Building Machine Learning Solutions on Google Cloud Platform, we've learned about all the fundamental principles of Data Engineering and how to use GCP services. At this point, you are more than ready to build a data...
