PDF Version DemoThe benefit of obtaining the Professional Machine Learning Engineer - Google Certification
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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:
- Automation of data preparation and model training/deployment
- Choose appropriate Google Cloud hardware components
- Feature engineering
- Optimizing data use and storage
- Design reliable, scalable, highly available ML solution
- Logging/management
- Building secure ML systems
- Serving
- A variety of component types - data collection; data management
- Choose appropriate Google Cloud software components
- SDLC best practices
- Design architecture that complies with regulatory and security concerns
- Exploration/analysis
- Data connections
- Monitoring
- Privacy implications of data usage
- Automation
- Identifying potential regulatory issues
- Selection of quotas and compute/accelerators with components
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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
- Implement training pipeline
- Performing data validation
- Hooking models into existing CI/CD deployment system
- Use CI/CD to test and deploy models
- Testing for target performance
- Organization and tracking experiments and pipeline runs
- Model binary options
- Hybrid or multi-cloud strategies
- Decoupling components with Cloud Build
- Implement serving pipeline
- Setup of trigger and pipeline schedule
- Track and audit metadata
- Identification of components, parameters, triggers, and compute needs
- Hooking into model and dataset versioning
- Orchestration framework
- Tuning compute performance
- Storing data and generated artifacts
- A/B and canary testing
- Google Cloud serving options
- Constructing and testing of parameterized pipeline definition in SDK
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks - Design and run experiments - Optimize model performance and generalization - Work with foundation models and generative AI techniques |
| Topic 2: Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows |
| Topic 3: Train and deploy models | 20% | - Use Vertex AI deployment features and infrastructure - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns - Configure training jobs and environments |
| Topic 4: Monitor and optimize AI solutions | 16% | - Monitor model performance, fairness, and drift - Monitor data quality and pipeline health - Optimize cost, latency, and resource usage - Troubleshoot and maintain production systems |
| Topic 5: 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 6: Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Address data privacy, compliance, and governance |
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