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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:

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

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing

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

• Define business success criteria
• Defining outcome of model predictions
• Success metrics
• Defining output use
• Identifying nonML solutions
• Identifying data sources
• Identify risks to feasibility and implementation of ML solution. Considerations include:
• Defining the input (features) and predicted output format
• Defining business problems
• Managing incorrect results
• Assessing ML solution readiness
• Define ML problem
• Determination of when a model is deemed unsuccessful
• Key results
• Assessing and communicating business impact
• Aligning with Google AI principles and practices (e.g. different biases)
• Assessing data readiness
• Defining problem type (classification, regression, clustering, etc.)

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

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.

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