Using Google Cloud ML Best Practices
In this chapter, we will discuss the best practices for implementing Machine Learning (ML) in Google Cloud. We will go through an implementation of a customer-trained ML model development process in GCP and provide recommendations throughout.
In this chapter, we will cover the following topics:
- ML environment setup
- ML data storage and processing
- ML model training
- ML model deployment
- ML workflow orchestration
- ML model continuous monitoring
This chapter aims to integrate the knowledge we have learned so far in this book and apply it to a customer-trained ML project. We will start by setting up the ML environment.
