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.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Exam Details
The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: 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) |
| Topic 2: Monitoring ML solutions | - Performance monitoring and drift detection - Model retraining strategies - Logging and alerting (Cloud Monitoring) |
| Topic 3: Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning |
| Topic 4: Automating and orchestrating ML pipelines | - CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) |
| Topic 5: Collaborating within and across teams to manage data and models | - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) |
| Topic 6: Serving and scaling models | - Online prediction (Vertex AI Prediction) - Batch prediction - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving |
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