“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)”
| ML Workflow | Google Cloud Service |
|---|---|
| Data Collection | Google Cloud Storage |
| Pub / Sub (streaming data) | |
| BigQuery | |
| Data transformation | Dataflow |
| Model training | custom models (vertex AI training and vertex AutoML) |
| Tuning and experiment tracking | Vertex AI hyperparameter tuning and Vertex AI Experiments |
| Deployment and monitoring | Vertex AI Predictions and Vertex AI Model monitoring |
| Orchestration and CI / CD | Vertex AI pipelines |
| Explanations and Responsible AI | Vertex Explainable AI, model cards |
