Official Google Cloud Certified Professional Machine Learning Engineer Study Guide

Chapter 5Architecting 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:
  • Data preprocessing (e.g., Dataflow, TensorFlow Extended [TFX], BigQuery)
• 2.3 Tracking and running ML experiments. Considerations include:
  • Choosing the appropriate Google Cloud environment for development and experimentation (e.g., Vertex AI Experiments, Kubeflow Pipelines, Vertex AI TensorBoard with TensorFlow and PyTorch) given the framework
• 4.1 Serving models. Considerations include:
  • Batch and online inference (e.g., Vertex AI, Dataflow, BigQuery ML, Dataproc)

In the previous chapter, we discussed various options to run your workloads in Google Cloud. In this chapter, we will talk about the best practices to design a reliable, scalable, and highly available ML solution on the Google Cloud Platform (GCP) using our artificial intelligence/machine learning (AI/ML) stack of services. We will introduce the Google AI/ML stack and talk about choosing appropriate services for ...