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Difficulty in Writing Professional Machine Learning Engineer - Google

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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:

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

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

Career Bonuses

The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.

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