Google Professional Machine Learning Engineer : Professional-Machine-Learning-Engineer

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:

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

Topics of Professional Machine Learning Engineer - Google

Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer exam dumps pdf will include the following topics:

• ML Model Development
• ML Solution Architecture
• ML Pipeline Automation & Orchestration
• ML Problem Framing
• Data Preparation and Processing
• ML Solution Monitoring, Optimization, and Maintenance

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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:

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

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