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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:

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

For more info read reference:

Google Web Services Website

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Professional Machine Learning Engineer - Google Certification Path

The associate level certification is focused on the fundamental skills of deploying, monitoring, and maintaining projects on Google Cloud. This certification is a good starting point for those new to cloud and can be used as a path to professional level certifications.

Professional certifications span key technical job functions and assess advanced skills in design, implementation, and management. These certifications are recommended for individuals with industry experience and familiarity with Google Cloud products and solutions.

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

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