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Prerequisites
The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise.
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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:
- Modeling techniques given interpretability requirements
- Unit tests for model training and serving
- Model explainability on Cloud AI Platform
- Scale model training and serving
- Tracking metrics during training
- Overfitting
- Hardware accelerators
- Model performance against baselines, simpler models, and across the time dimension
- Choice of framework and model
- Transfer learning
- Build a model
- Retraining/redeployment evaluation
- Training a model as a job in different environments
- Distributed training
- Productionizing
- Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
- Model generalization
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:
- Decoupling components with Cloud Build
- Testing for target performance
- Identification of components, parameters, triggers, and compute needs
- Use CI/CD to test and deploy models
- Storing data and generated artifacts
- Performing data validation
- Google Cloud serving options
- Implement serving pipeline
- Tuning compute performance
- Constructing and testing of parameterized pipeline definition in SDK
- Model/dataset lineage
- Organization and tracking experiments and pipeline runs
- Hooking models into existing CI/CD deployment system
- Orchestration framework
- Implement training pipeline
- Hooking into model and dataset versioning
- Setup of trigger and pipeline schedule
- Track and audit metadata
- Hybrid or multi-cloud strategies
- A/B and canary testing
- Model binary options
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Serving and scaling models | - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving - Batch prediction - Online prediction (Vertex AI Prediction) |
| Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Monitoring ML solutions | - Model retraining strategies - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) |
| Collaborating within and across teams to manage data and models | - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) |
| Architecting low-code ML solutions | - Implementing BigQuery ML for basic models - AutoML capabilities and implementation - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) |
| Automating and orchestrating ML pipelines | - CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) |
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