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Topics of Professional Machine Learning Engineer - Google
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- ML Solution Architecture
- Data Preparation and Processing
- ML Solution Monitoring, Optimization, and Maintenance
- ML Pipeline Automation & Orchestration
- ML Model Development
- ML Problem Framing
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How to book the Professional Machine Learning Engineer - Google
To apply for the Professional Machine Learning Engineer - Google, You have to follow these steps:
- Step 1: Go to the Google Official Site
- Step 2: Read the instruction carefully
- Step 3: Follow the given steps
- Step 4: Apply for the Professional Machine Learning Engineer Exam
Exam Details
The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Collaborate to manage data and models | 16% | - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Topic 2: Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs |
| Topic 3: Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Select appropriate model architectures and frameworks - Design and run experiments - Work with foundation models and generative AI techniques |
| Topic 4: Train and deploy models | 20% | - Implement generative AI deployment patterns - Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure - Configure training jobs and environments |
| Topic 5: Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Automate retraining and model updates |
| Topic 6: 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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