Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
Design pipeline. Considerations include:
- Identification of components, parameters, triggers, and compute needs
- Hooking into model and dataset versioning
- Organization and tracking experiments and pipeline runs
- A/B and canary testing
- Constructing and testing of parameterized pipeline definition in SDK
- Hybrid or multi-cloud strategies
- Setup of trigger and pipeline schedule
- Implement training pipeline
- Hooking models into existing CI/CD deployment system
- Model/dataset lineage
- Orchestration framework
- Model binary options
- Tuning compute performance
- Decoupling components with Cloud Build
- Track and audit metadata
- Google Cloud serving options
- Storing data and generated artifacts
- Testing for target performance
- Implement serving pipeline
- Use CI/CD to test and deploy models
- Performing data validation
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Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: ML model development | - Evaluation
|
| Topic 2: Deployment and operations | - Monitoring and maintenance
|
| Topic 3: Data preparation and processing | - Data ingestion and pipelines
|
| Topic 4: Designing ML solutions | - ML architecture design
|
| Topic 5: ML pipeline automation and orchestration | - Pipeline design
|








