PDF Version DemoUnderstanding 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:
- Organization and tracking experiments and pipeline runs
- Tuning compute performance
- Testing for target performance
- Orchestration framework
- Storing data and generated artifacts
- Track and audit metadata
- Hooking into model and dataset versioning
- Setup of trigger and pipeline schedule
- Decoupling components with Cloud Build
- Model/dataset lineage
- Hooking models into existing CI/CD deployment system
- Model binary options
- A/B and canary testing
- Implement serving pipeline
- Implement training pipeline
- Performing data validation
- Use CI/CD to test and deploy models
- Constructing and testing of parameterized pipeline definition in SDK
- Google Cloud serving options
- Hybrid or multi-cloud strategies
- Identification of components, parameters, triggers, and compute needs
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.
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Professional Machine Learning Engineer - Google Certified salary
The estimated average salary of Professional Machine Learning Engineer - Google is listed below:
- Europe: 97,000 EURO
- United States: 114,000 USD
- England: 87,200 POUND
- India: 8,580,000 INR
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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 - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation |
| Topic 2: Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning |
| Topic 3: Serving and scaling models | - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Batch prediction - Online prediction (Vertex AI Prediction) |
| Topic 4: Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) |
| Topic 5: 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 6: Monitoring ML solutions | - Performance monitoring and drift detection - Model retraining strategies - Logging and alerting (Cloud Monitoring) |
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