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:

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

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 Data Preparation and Processing

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

• Design data pipelines
• Batching and streaming data pipelines at scale
• Visualization
• Database migration
• Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
• Feature crosses
• Encoding structured data types
• Monitoring/changing deployed pipelines
• Feature selection
• Streaming data (e.g. from IoT devices)
• Data exploration (EDA)
• Statistical fundamentals at scale
• Data privacy and compliance
• Build data pipelines
• Data leakage and augmentation
• Data validation
• Handling missing data
• Class imbalance
• Transformations (TensorFlow Transform)
• Evaluation of data quality and feasibility
• Feature engineering
• Handling outliers
• Managing large samples (TFRecords)
• Data ingestion

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The candidates for the Google Professional Machine Learning Engineer certification can find everything they need to efficiently prepare for the qualifying test on the official website. The most recommended resource offered by the vendor is the Machine Learning Engineer learning path. It contains both lessons and practical labs for a comprehensive understanding of the exam content. Moreover, the students can take advantage of the sample questions designed to help the potential test takers familiarize themselves with the possible exam questions. Finally, the applicants can opt for the Machine Learning Engineer Prep Webinar to join the Google experts and recently certified professionals for the tips and insights on the Machine Learning models, data processing systems, solution quality, and more.

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