Official Google Cloud Certified Professional Machine Learning Engineer Study Guide

Chapter 13Maintaining ML Solutions

GOOGLE CLOUD PROFESSIONAL MACHINE LEARNING ENGINEER EXAM OBJECTIVES COVERED IN THIS CHAPTER:

• 2.1 Exploring and preprocessing organization‐wide data (e.g., Cloud Storage, BigQuery, Cloud Spanner, Cloud SQL, Apache Spark, Apache Hadoop). Considerations include:
  • Creating and consolidating features in Vertex AI Feature Store
• 4.2 Scaling online model serving. Considerations include:
  • Vertex AI Feature Store
• 5.2 Automating model retraining. Considerations include:
  • Determining an appropriate retraining policy
  • Continuous integration and continuous delivery (CI/CD) model deployment (e.g., Cloud Build, Jenkins)
• 6.2 Monitoring, testing, and troubleshooting ML solutions. Considerations include:
  • Establishing continuous evaluation metrics (e.g., Vertex AI Model Monitoring, Explainable AI)
  • Common training and serving errors

In the previous chapters, you learned about training a model, deploying strategies, and monitoring deployed models. In this chapter, we will discuss some of the concepts that were not covered before but are important for ...