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The benefit of obtaining the Professional Machine Learning Engineer - Google Certification

• More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
• 87% of Google Cloud certified individuals are more confident about their cloud skills
• Professional Cloud Architect was the highest paying certification of 2020 and 2019

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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:

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

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

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