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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:
- Model/dataset lineage
- Orchestration framework
- Storing data and generated artifacts
- Implement training pipeline
- Track and audit metadata
- Google Cloud serving options
- Identification of components, parameters, triggers, and compute needs
- Hooking models into existing CI/CD deployment system
- Testing for target performance
- Hooking into model and dataset versioning
- Setup of trigger and pipeline schedule
- Decoupling components with Cloud Build
- Organization and tracking experiments and pipeline runs
- Constructing and testing of parameterized pipeline definition in SDK
- A/B and canary testing
- Model binary options
- Use CI/CD to test and deploy models
- Tuning compute performance
- Performing data validation
- Implement serving pipeline
- Hybrid or multi-cloud strategies
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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For more info read reference:
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Collaborate to manage data and models | 16% | - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Topic 2: Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools |
| Topic 3: Scale prototypes into AI models | 18% | - Work with foundation models and generative AI techniques - Select appropriate model architectures and frameworks - Optimize model performance and generalization - Design and run experiments |
| Topic 4: Train and deploy models | 20% | - Implement generative AI deployment patterns - Use Vertex AI deployment features and infrastructure - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments |
| Topic 5: Monitor and optimize AI solutions | 16% | - Monitor model performance, fairness, and drift - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health - Troubleshoot and maintain production systems |
| Topic 6: Automate and orchestrate ML pipelines | 18% | - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Implement CI/CD for ML systems |
