ML Mindset

Short summary

Pragmatic ML development starts with defining business ROI, not chasing model accuracy. Build simple baselines first and increase complexity only when justified by business value. In production, containerize your model, set up monitoring and rollback procedures, and maintain clear documentation.

• •Define business ROI before building a model; validation accuracy alone doesn't pay the bills
• •Start with simple baselines (linear regression) and increase complexity only when justified by business value
• •Prioritize explainability using SHAP/LIME; unexplainable models are too risky to deploy
• •In production: containerization, versioning, monitoring for drift, and comprehensive documentation are essential

Generated with AI, which can make mistakes.