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Career Bonuses
The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.
Topics of Professional Machine Learning Engineer - Google
Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer exam dumps pdf will include the following topics:
- ML Problem Framing
- ML Model Development
- ML Pipeline Automation & Orchestration
- ML Solution Monitoring, Optimization, and Maintenance
- Data Preparation and Processing
- ML Solution Architecture
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:
- Track and audit metadata
- Setup of trigger and pipeline schedule
- Organization and tracking experiments and pipeline runs
- Orchestration framework
- Use CI/CD to test and deploy models
- Hybrid or multi-cloud strategies
- Decoupling components with Cloud Build
- Hooking into model and dataset versioning
- Identification of components, parameters, triggers, and compute needs
- Hooking models into existing CI/CD deployment system
- Constructing and testing of parameterized pipeline definition in SDK
- Performing data validation
- Google Cloud serving options
- Model binary options
- Implement serving pipeline
- Tuning compute performance
- Model/dataset lineage
- A/B and canary testing
- Storing data and generated artifacts
- Implement training pipeline
- Testing for target performance
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data preparation and processing | - Feature engineering
|
| ML pipeline automation and orchestration | - Pipeline design
|
| Deployment and operations | - Monitoring and maintenance
|
| ML model development | - Model training and tuning
|
| Designing ML solutions | - ML architecture design
|

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