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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:

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

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

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The Google Professional Machine Learning Engineer certification is developed to validate the ability of the specialists to design, build, and productionize the Machine Learning models to solve business challenges with the help of Google Cloud technologies as well as their knowledge of the proven Machine Learning models & techniques. Specifically, this certificate equips the candidates with an understanding of all the aspects related to data pipeline interaction, model architecture, as well as metrics interpretation. It also provides the target individuals with the comprehension of the basic concepts of application development, data engineering, infrastructure management, and data governance. To get certified, the individuals need to take one qualifying exam.

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