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:

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

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

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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.