Official Google Cloud Certified Professional Machine Learning Engineer Study Guide

Chapter 6Building Secure ML Pipelines

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:
  • Privacy implications of data usage and/or collection (e.g., handling sensitive data such as personally identifiable information [PII] and protected health information [PHI])
• 2.2 Model prototyping using Jupyter notebooks. Considerations include:
  • Applying security best practices in Vertex AI Workbench
• 4.2 Scaling online model serving. Considerations include:
  • Vertex AI public and private endpoints
• 6.1 Identifying risks to ML solutions. Considerations include:
  • Building secure ML systems (e.g., protecting against unintentional exploitation of data or models, hacking)

In this chapter, first we will talk about secure strategies to manage your data on Google Cloud Platform, from storage to processing and ML model development. We will also briefly talk about how you can use ...