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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration

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

Design pipeline. Considerations include:

• Tuning compute performance
• A/B and canary testing
• Implement serving pipeline
• Organization and tracking experiments and pipeline runs
• Use CI/CD to test and deploy models
• Setup of trigger and pipeline schedule
• Decoupling components with Cloud Build
• Identification of components, parameters, triggers, and compute needs
• Google Cloud serving options
• Model binary options
• Hooking models into existing CI/CD deployment system
• Performing data validation
• Hooking into model and dataset versioning
• Constructing and testing of parameterized pipeline definition in SDK
• Model/dataset lineage
• Hybrid or multi-cloud strategies
• Orchestration framework
• Track and audit metadata
• Storing data and generated artifacts
• Implement training pipeline
• Testing for target performance

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:

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

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

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