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Prerequisites

The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise.

For more info read reference:

Google Web Services Website

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Topics of Professional Machine Learning Engineer - Google

Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer exam dumps pdf will include the following topics:

• Data Preparation and Processing
• ML Problem Framing
• ML Pipeline Automation & Orchestration
• ML Solution Monitoring, Optimization, and Maintenance
• ML Solution Architecture
• ML Model Development

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 Pipeline Automation & Orchestration

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

Design pipeline. Considerations include:

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

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