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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:
- Retraining/redeployment evaluation
- Modeling techniques given interpretability requirements
- Model explainability on Cloud AI Platform
- Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
- Training a model as a job in different environments
- Tracking metrics during training
- Model generalization
- Transfer learning
- Model performance against baselines, simpler models, and across the time dimension
- Hardware accelerators
- Productionizing
- Scale model training and serving
- Unit tests for model training and serving
- Choice of framework and model
- Overfitting
- Distributed training
- Build a model
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Professional Machine Learning Engineer - Google Certified salary
The estimated average salary of Professional Machine Learning Engineer - Google is listed below:
- India: 8,580,000 INR
- Europe: 97,000 EURO
- England: 87,200 POUND
- United States: 114,000 USD
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Architecting low-code ML solutions | - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation - Implementing BigQuery ML for basic models |
| 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 |
| Automating and orchestrating ML pipelines | - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines - CI/CD for ML systems |
| Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Monitoring ML solutions | - Performance monitoring and drift detection - Model retraining strategies - Logging and alerting (Cloud Monitoring) |
| Serving and scaling models | - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving - Batch prediction - Online prediction (Vertex AI Prediction) |
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