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How to study the Professional Machine Learning Engineer - Google

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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:

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

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

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