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ML Lifecycle
Collect Data (Data Engineers Role)
- Gather raw data from systems (databases, APIs, sensors, logs).
- Ensure sources are reliable and updated.
Clean & Prepare
- Handle missing values, outliers, and noise.
- Feature engineering: create new features, scale/encode as needed.
- Data splitting (train/validation/test).
Train Model
- Choose algorithm (supervised, unsupervised, reinforcement, etc.).
- Train on training set, tune hyperparameters.
Evaluate
- Use appropriate metrics:
- Classification → Accuracy, Precision, Recall, F1.
- Regression → RMSE, MAE, R².
- Cross-validation for robustness.
Deploy
- Make model accessible via API, batch jobs, or embedded in applications.
- Consider scaling (cloud, containers, edge devices).
Monitor & Improve
- Track data drift, concept drift, and model performance decay.
- Automate retraining pipelines (MLOps).
- Capture feedback loop to improve features and models.