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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development

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

• Distributed training
• Overfitting
• Model generalization
• Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
• Tracking metrics during training
• Retraining/redeployment evaluation
• Productionizing
• Choice of framework and model
• Scale model training and serving
• Hardware accelerators
• Unit tests for model training and serving
• Model performance against baselines, simpler models, and across the time dimension
• Training a model as a job in different environments
• Build a model
• Model explainability on Cloud AI Platform
• Modeling techniques given interpretability requirements
• Transfer learning

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

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

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