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Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Professional Machine Learning Engineer - Google Certification Path
The associate level certification is focused on the fundamental skills of deploying, monitoring, and maintaining projects on Google Cloud. This certification is a good starting point for those new to cloud and can be used as a path to professional level certifications.
Professional certifications span key technical job functions and assess advanced skills in design, implementation, and management. These certifications are recommended for individuals with industry experience and familiarity with Google Cloud products and solutions.
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: 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) |
| Topic 2: Automating and orchestrating ML pipelines | - CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) |
| Topic 3: Scaling prototypes into ML models | - Hyperparameter tuning - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Topic 4: Monitoring ML solutions | - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection - Model retraining strategies |
| Topic 5: Serving and scaling models | - Batch prediction - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving - Online prediction (Vertex AI Prediction) |
| Topic 6: 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 |
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