Professional Machine Learning Engineer - Google Certified salary
The estimated average salary of Professional Machine Learning Engineer - Google is listed below:
- England: 87,200 POUND
- Europe: 97,000 EURO
- India: 8,580,000 INR
- United States: 114,000 USD
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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The Google Professional Machine Learning Engineer certification is developed to validate the ability of the specialists to design, build, and productionize the Machine Learning models to solve business challenges with the help of Google Cloud technologies as well as their knowledge of the proven Machine Learning models & techniques. Specifically, this certificate equips the candidates with an understanding of all the aspects related to data pipeline interaction, model architecture, as well as metrics interpretation. It also provides the target individuals with the comprehension of the basic concepts of application development, data engineering, infrastructure management, and data governance. To get certified, the individuals need to take one qualifying exam.
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
| Topic 1: Serving and scaling models | - Online prediction (Vertex AI Prediction)
- Batch prediction
- Hardware accelerators (GPU/TPU) in serving
- Model optimization (Quantization, Distillation)
|
| Topic 2: 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 3: Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn)
- Hyperparameter tuning
- Training at scale (Distributed training, TPUs)
|
| Topic 4: Monitoring ML solutions | - Performance monitoring and drift detection
- Logging and alerting (Cloud Monitoring)
- Model retraining strategies
|
| Topic 5: 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
|
| Topic 6: Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines
- Vertex AI Pipelines (Kubeflow Pipelines)
- CI/CD for ML systems
|