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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.

How to study the Professional Machine Learning Engineer - Google

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

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

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:

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