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Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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The benefit of obtaining the Professional Machine Learning Engineer - Google Certification
- 87% of Google Cloud certified individuals are more confident about their cloud skills
- Professional Cloud Architect was the highest paying certification of 2020 and 2019
- More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Automate and orchestrate ML pipelines | 18% | - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Automate retraining and model updates - Implement CI/CD for ML systems |
| Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Architect low-code AI solutions | 12% | - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs |
| 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 |
| Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage |
| Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks - Design and run experiments - Work with foundation models and generative AI techniques - Optimize model performance and generalization |
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