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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Solution Architecture
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- SDLC best practices
- Choose appropriate Google Cloud hardware components
- Identifying potential regulatory issues
- Data connections
- Logging/management
- Automation of data preparation and model training/deployment
- Building secure ML systems
- Design reliable, scalable, highly available ML solution
- Selection of quotas and compute/accelerators with components
- A variety of component types - data collection; data management
- Feature engineering
- Serving
- Exploration/analysis
- Privacy implications of data usage
- Monitoring
- Optimizing data use and storage
- Design architecture that complies with regulatory and security concerns
- Automation
- Choose appropriate Google Cloud software components
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Implement generative AI deployment patterns - Use Vertex AI deployment features and infrastructure |
| Topic 3: Scale prototypes into AI models | 18% | - Design and run experiments - Optimize model performance and generalization - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques |
| Topic 4: 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 |
| Topic 5: Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Address data privacy, compliance, and governance |
| Topic 6: Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder |
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