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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
- Data exploration (EDA)
- Visualization
- Streaming data (e.g. from IoT devices)
- Encoding structured data types
- Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
- Handling missing data
- Transformations (TensorFlow Transform)
- Data validation
- Feature engineering
- Database migration
- Feature crosses
- Design data pipelines
- Build data pipelines
- Statistical fundamentals at scale
- Data privacy and compliance
- Handling outliers
- Data ingestion
- Feature selection
- Evaluation of data quality and feasibility
- Data leakage and augmentation
- Managing large samples (TFRecords)
- Monitoring/changing deployed pipelines
- Class imbalance
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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:
- Google Cloud serving options
- Implement training pipeline
- Decoupling components with Cloud Build
- Track and audit metadata
- A/B and canary testing
- Identification of components, parameters, triggers, and compute needs
- Hooking into model and dataset versioning
- Hybrid or multi-cloud strategies
- Model/dataset lineage
- Tuning compute performance
- Constructing and testing of parameterized pipeline definition in SDK
- Performing data validation
- Storing data and generated artifacts
- Orchestration framework
- Use CI/CD to test and deploy models
- Model binary options
- Implement serving pipeline
- Hooking models into existing CI/CD deployment system
- Organization and tracking experiments and pipeline runs
- Testing for target performance
- Setup of trigger and pipeline schedule
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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