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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:

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

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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.

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

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