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Google Professional Machine Learning Engineer Certification Exam is a professional certification that tests the knowledge and skills of individuals in the field of machine learning. Professional-Machine-Learning-Engineer exam is designed to evaluate the proficiency of candidates in various aspects of machine learning, including data processing, modeling, and deployment. Google Professional Machine Learning Engineer certification is offered by Google Cloud, a subsidiary of Google that provides cloud computing services to businesses and individuals.
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To be eligible for the Google Professional-Machine-Learning-Engineer Certification Exam, you need to have at least three years of experience in developing and deploying machine learning models on Google Cloud Platform or a similar platform. You should also have experience in programming languages such as Python, Java, or C++, and have a good understanding of machine learning concepts such as supervised and unsupervised learning, deep learning, and reinforcement learning.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Address data privacy, compliance, and governance |
| Train and deploy models | 20% | - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction |
| Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques - Design and run experiments - Optimize model performance and generalization |
| 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 |
| Automate and orchestrate ML pipelines | 18% | - Use Vertex AI Pipelines, TFX, and other orchestration tools - Implement CI/CD for ML systems - Automate retraining and model updates - Design end-to-end ML workflows |
| Monitor and optimize AI solutions | 16% | - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift - Monitor data quality and pipeline health |
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