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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:
- Use CI/CD to test and deploy models
- Hybrid or multi-cloud strategies
- Model binary options
- Testing for target performance
- Storing data and generated artifacts
- Performing data validation
- Setup of trigger and pipeline schedule
- Implement serving pipeline
- Decoupling components with Cloud Build
- Hooking into model and dataset versioning
- Google Cloud serving options
- Organization and tracking experiments and pipeline runs
- Orchestration framework
- Hooking models into existing CI/CD deployment system
- Constructing and testing of parameterized pipeline definition in SDK
- Model/dataset lineage
- Track and audit metadata
- Identification of components, parameters, triggers, and compute needs
- Tuning compute performance
- A/B and canary testing
- Implement training pipeline
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For more info read reference:
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Professional Machine Learning Engineer - Google Certified salary
The estimated average salary of Professional Machine Learning Engineer - Google is listed below:
- Europe: 97,000 EURO
- United States: 114,000 USD
- India: 8,580,000 INR
- England: 87,200 POUND
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Address data privacy, compliance, and governance |
| Topic 2: Scale prototypes into AI models | 18% | - Work with foundation models and generative AI techniques - Design and run experiments - Optimize model performance and generalization - Select appropriate model architectures and frameworks |
| Topic 3: Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Troubleshoot and maintain production systems - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift |
| Topic 4: Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder |
| Topic 5: Train and deploy models | 20% | - Implement generative AI deployment patterns - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure |
| Topic 6: Automate and orchestrate ML pipelines | 18% | - Automate retraining and model updates - Design end-to-end ML workflows - Use Vertex AI Pipelines, TFX, and other orchestration tools - Implement CI/CD for ML systems |
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