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Step 1: Setup Feature Store
A feature store centralizes ML features for training and serving, ensuring consistency between training and inference.
Configuration:
• Store Name: Identifier for the store (e.g., "customer_features", "fraud_detection_store")
• Feature Group: Logical grouping of related features (e.g., "user_behavior", "transaction_patterns")
• Storage Backend: Choose your feature store platform (Feast/Tecton/Hopsworks)
• Serving Mode: Online (real-time inference), Offline (batch), or Both
• Versioning: ✓ MUST be enabled to track feature changes
💡 Tip: Use "Both" serving mode if you need real-time predictions AND batch training.
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Step 2: Define Training Pipeline
Create an automated pipeline that handles data ingestion, preprocessing, training, and validation.
Configuration:
• Pipeline Name: Descriptive name (e.g., "churn_prediction", "fraud_classifier")
• Orchestrator: Choose workflow engine (Kubeflow/MLflow/Airflow)
• Training Framework: Scikit-learn (tabular), TensorFlow/PyTorch (deep learning)
• Hyperparameter Tuning: Grid Search, Random Search, or Bayesian optimization
💡 Tip: Bayesian optimization is most efficient for complex hyperparameter spaces.
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Step 3: Configure Model Registry
A model registry stores trained models with versioning, metadata, and lifecycle management.
Configuration:
• Registry Tool: Where models are stored (MLflow/Neptune.ai/Weights & Biases)
• Model Version: Semantic versioning (e.g., "1.0.0")
• Stage: Staging (testing) → Production (live) → Archived (deprecated)
• Approval Workflow: ✓ MUST be enabled for production safety
💡 Tip: Always use Staging before Production. Never skip the approval step!
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Step 4: Deployment Strategy
Configure how models are released to production with safety mechanisms for rollback.
Configuration:
• Deployment Type: Canary (gradual 10%→100%), Blue-Green (instant switch), Shadow (parallel)
• Traffic Split: % of traffic to new model (0-100)
• Rollback Threshold: Error % that triggers automatic rollback (0-100)
• Serving Platform: Seldon Core, KServe, or SageMaker
💡 Tip: Start with 10% traffic split for canary. Set rollback threshold at 5% for safety.
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Step 5: Model Monitoring
Set up continuous monitoring to detect when model performance degrades in production.
Configuration:
• Monitoring Metrics: Check ALL - Data Drift, Concept Drift, Performance
• Alert Threshold: PSI/drift score that triggers alert (e.g., 0.15)
• Retraining Trigger: When to automatically retrain (On Drift/On Performance/Scheduled)
💡 Tip: Alert threshold of 0.15 PSI is industry standard. Check ALL monitoring metrics!
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Step 6: Governance & Compliance
Ensure models are explainable, fair, and compliant with regulations.
Configuration:
• Explainability Tool: How to interpret predictions (SHAP/LIME/ELI5)
• Bias Detection: Fairlearn, AIF360, or What-If Tool
• Compliance Framework: GDPR (EU), CCPA (California), HIPAA (Healthcare)
• Audit Trail: ✓ MUST be enabled for regulatory compliance
💡 Tip: SHAP is most widely accepted for explainability. Always enable audit trail!