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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:

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

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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Solution Architecture

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

• Exploration/analysis
• Choose appropriate Google Cloud software components
• Building secure ML systems
• Monitoring
• Choose appropriate Google Cloud hardware components
• Selection of quotas and compute/accelerators with components
• Privacy implications of data usage
• Automation
• Logging/management
• A variety of component types - data collection; data management
• SDLC best practices
• Automation of data preparation and model training/deployment
• Data connections
• Optimizing data use and storage
• Identifying potential regulatory issues
• Serving
• Design reliable, scalable, highly available ML solution
• Feature engineering
• Design architecture that complies with regulatory and security concerns

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

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