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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:
- Evaluation of data quality and feasibility
- Data validation
- Design data pipelines
- Feature engineering
- Streaming data (e.g. from IoT devices)
- Data privacy and compliance
- Handling outliers
- Data leakage and augmentation
- Database migration
- Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
- Visualization
- Managing large samples (TFRecords)
- Data ingestion
- Encoding structured data types
- Transformations (TensorFlow Transform)
- Data exploration (EDA)
- Feature selection
- Build data pipelines
- Feature crosses
- Class imbalance
- Handling missing data
- Statistical fundamentals at scale
- Monitoring/changing deployed pipelines
- Batching and streaming data pipelines at scale
Professional Machine Learning Engineer - Google Certified salary
The estimated average salary of Professional Machine Learning Engineer - Google is listed below:
- United States: 114,000 USD
- England: 87,200 POUND
- India: 8,580,000 INR
- Europe: 97,000 EURO
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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:
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- A/B and canary testing
- Model/dataset lineage
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- Identification of components, parameters, triggers, and compute needs
- Setup of trigger and pipeline schedule
- Orchestration framework
- Track and audit metadata
- Hooking into model and dataset versioning
- Implement serving pipeline
- Hybrid or multi-cloud strategies
- Organization and tracking experiments and pipeline runs
- Hooking models into existing CI/CD deployment system
- Performing data validation
- Use CI/CD to test and deploy models
- Tuning compute performance
- Implement training pipeline
- Decoupling components with Cloud Build
- Constructing and testing of parameterized pipeline definition in SDK
- Model binary options
- Google Cloud serving options
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Architecting low-code ML solutions | - AutoML capabilities and implementation - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) |
| Collaborating within and across teams to manage data and models | - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) - Data management and governance |
| Serving and scaling models | - Batch prediction - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving - Online prediction (Vertex AI Prediction) |
| Automating and orchestrating ML pipelines | - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines - CI/CD for ML systems |
| Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Training at scale (Distributed training, TPUs) - Hyperparameter tuning |
| Monitoring ML solutions | - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) - Model retraining strategies |
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