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

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

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:

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

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