Summary
In this chapter, we discussed the best practices for implementing ML in Google Cloud, with a focus on custom-trained models based on your data and code.
This chapter concludes Part 3 of this book, in which we have discussed Google BQ and BQML for training ML models from structured data, Google ML training frameworks such as TensorFlow and Keras, the Google ML training suite Vertex AI, Google Cloud ML APIs, and the best ML practices in Google Cloud.
In the fourth part of this book, we will prepare for the Google Cloud Certified Professional ML Engineer certification by understanding the certification’s requirements and deep diving into some of the certification’s practice questions.
