• Not all hard problems need ML—validate that adaptive learning actually solves your business problem before building.
• Start with simple models (logistic regression, XGBoost) over cutting-edge architectures; prove concept viability first.
• Data infrastructure matters as much as the model; invest in sourcing, labeling, and validation before training.
• Build feedback loops and track real-world metrics from day one; plan for retraining and model iteration.
• Calculate unit economics upfront—inference, training, and infrastructure costs must align with your business model to scale.
