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The benefit of obtaining the Professional Machine Learning Engineer - Google Certification

• 87% of Google Cloud certified individuals are more confident about their cloud skills
• Professional Cloud Architect was the highest paying certification of 2020 and 2019
• More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work

Professional Machine Learning Engineer - Google Certified salary

The estimated average salary of Professional Machine Learning Engineer - Google is listed below:

• United States: 114,000 USD
• India: 8,580,000 INR
• England: 87,200 POUND
• Europe: 97,000 EURO

Reference: https://cloud.google.com/certification/guides/machine-learning-engineer

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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:

• Design reliable, scalable, highly available ML solution
• Serving
• Feature engineering
• Design architecture that complies with regulatory and security concerns
• A variety of component types - data collection; data management
• Privacy implications of data usage
• Building secure ML systems
• Optimizing data use and storage
• Data connections
• Logging/management
• Choose appropriate Google Cloud hardware components
• Identifying potential regulatory issues
• Exploration/analysis
• SDLC best practices
• Automation of data preparation and model training/deployment
• Choose appropriate Google Cloud software components
• Automation
• Selection of quotas and compute/accelerators with components
• Monitoring

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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 binary options
• Hooking models into existing CI/CD deployment system
• Implement training pipeline
• Hybrid or multi-cloud strategies
• Decoupling components with Cloud Build
• Storing data and generated artifacts
• Use CI/CD to test and deploy models
• Identification of components, parameters, triggers, and compute needs
• Orchestration framework
• Model/dataset lineage
• Implement serving pipeline
• Testing for target performance
• Performing data validation
• Track and audit metadata
• Hooking into model and dataset versioning
• Tuning compute performance
• A/B and canary testing
• Organization and tracking experiments and pipeline runs
• Setup of trigger and pipeline schedule
• Google Cloud serving options
• Constructing and testing of parameterized pipeline definition in SDK

Google Professional-Machine-Learning-Engineer Exam Syllabus Topics: