Google Cloud
Google Cloud Professional Machine Learning Engineer
Google's Professional Machine Learning Engineer certification validates the ability to design, build, productionize, and optimize ML solutions on Google Cloud, including generative AI on foundation models. Six domains span low-code AI, data and model collaboration, scaling prototypes, serving, automating pipelines, and monitoring.
Exam Blueprint
Architecting low-code AI solutions
Build ML models with BigQuery ML, ML APIs, foundation models, and AutoML. Covers BigQuery ML model t...
Collaborating within and across teams to manage data and models
Explore and preprocess data on Cloud Storage, BigQuery, Spanner, Cloud SQL, Apache Spark, Apache Had...
Scaling prototypes into ML models
Build, train, and choose hardware for ML models. Covers data ingestion, model architecture, training...
Serving and scaling models
Serve and scale models for online and batch prediction. Vertex AI Endpoints, container customization...
Automating and orchestrating ML pipelines
Develop end-to-end ML pipelines with Vertex AI Pipelines. Automate model retraining triggers. Track ...
Monitoring AI solutions
Identify risks to AI solutions (data, model, fairness, privacy). Monitor, test, and troubleshoot AI ...
Question Bank
35
Recall
205
Application
61
Analysis
What You'll Study
Architecting low-code AI solutions
- 1.1Developing ML models by using BigQuery ML
- 1.2Building AI solutions by using ML APIs or foundation models
- 1.3Training models by using AutoML
Collaborating within and across teams to manage data and models
- 2.1Exploring and preprocessing organization-wide data
- 2.2Model prototyping using Jupyter notebooks
- 2.3Tracking and running ML experiments
Scaling prototypes into ML models
- 3.1Building models
- 3.2Training models
- 3.3Choosing appropriate hardware for training
Serving and scaling models
- 4.1Serving models
- 4.2Scaling online model serving
Automating and orchestrating ML pipelines
- 5.1Developing end-to-end ML pipelines
- 5.2Automating model retraining
- 5.3Tracking and auditing metadata
Monitoring AI solutions
- 6.1Identifying risks to AI solutions
- 6.2Monitoring, testing, and troubleshooting AI solutions
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