Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Solution Architecture

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

• Choose appropriate Google Cloud hardware components
• Monitoring
• Automation
• Building secure ML systems
• A variety of component types - data collection; data management
• Identifying potential regulatory issues
• Data connections
• Serving
• Choose appropriate Google Cloud software components
• Optimizing data use and storage
• Feature engineering
• Design reliable, scalable, highly available ML solution
• SDLC best practices
• Exploration/analysis
• Design architecture that complies with regulatory and security concerns
• Selection of quotas and compute/accelerators with components
• Logging/management
• Privacy implications of data usage
• Automation of data preparation and model training/deployment

How to Prepare For Professional Machine Learning Engineer - Google

Preparation Guide for Professional Machine Learning Engineer - Google

Introduction for Professional Machine Learning Engineer - Google

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.

The Professional Machine Learning Engineer exam assesses your ability to:

• Frame ML problems
• Architect ML solutions
• Develop ML models
• Prepare and process data
• Automate & orchestrate ML pipelines
• Monitor, optimize, and maintain ML solutions

We prepare Google Professional-Machine-Learning-Engineer practice exams and Google Professional-Machine-Learning-Engineer practice exams to prepare you for all these requirements.

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

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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:

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