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Difficulty in Writing Professional Machine Learning Engineer - Google
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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Aligning with Google AI principles and practices (e.g. different biases)
- Define business success criteria
- Key results
- Determination of when a model is deemed unsuccessful
- Assessing data readiness
- Defining outcome of model predictions
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Identifying data sources
- Defining business problems
- Identifying nonML solutions
- Defining output use
- Define ML problem
- Managing incorrect results
- Defining problem type (classification, regression, clustering, etc.)
- Defining the input (features) and predicted output format
- Success metrics
- Assessing and communicating business impact
- Assessing ML solution readiness
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Monitoring ML solutions | - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) - Model retraining strategies |
| Serving and scaling models | - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving - Batch prediction |
| 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) |
| Collaborating within and across teams to manage data and models | - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) - Data management and governance |
| Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) |
