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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:
- Defining problem type (classification, regression, clustering, etc.)
- Assessing ML solution readiness
- Defining business problems
- Managing incorrect results
- Assessing and communicating business impact
- Defining outcome of model predictions
- Identifying nonML solutions
- Determination of when a model is deemed unsuccessful
- Defining the input (features) and predicted output format
- Define business success criteria
- Defining output use
- Key results
- Success metrics
- Identifying data sources
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Assessing data readiness
- Define ML problem
- Aligning with Google AI principles and practices (e.g. different biases)
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Build a model
- Model generalization
- Overfitting
- Unit tests for model training and serving
- Modeling techniques given interpretability requirements
- Tracking metrics during training
- Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
- Retraining/redeployment evaluation
- Scale model training and serving
- Training a model as a job in different environments
- Choice of framework and model
- Model explainability on Cloud AI Platform
- Model performance against baselines, simpler models, and across the time dimension
- Hardware accelerators
- Transfer learning
- Productionizing
- Distributed training
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Architecting low-code ML solutions | - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - Implementing BigQuery ML for basic models - AutoML capabilities and implementation |
| Automating and orchestrating ML pipelines | - CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) |
| Collaborating within and across teams to manage data and models | - Version control and reproducibility (e.g., DVC, MLOps) - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers |
| Monitoring ML solutions | - Model retraining strategies - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection |
| Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Serving and scaling models | - Hardware accelerators (GPU/TPU) in serving - Batch prediction - Online prediction (Vertex AI Prediction) - Model optimization (Quantization, Distillation) |








