Most machine learning projects that fail do so for reasons that have little to do with algorithms: unclear goals, poor data and models that never reach production.
Define success before you train
Agree on a business metric, such as fewer manual reviews, lower churn or faster delivery, and a baseline to beat. A model that is 95% accurate on the wrong target is still a failure.
Treat data as the product
- Audit data sources for gaps, bias and leakage
- Version datasets alongside code
- Label consistently with clear guidelines
- Keep a hold-out set that reflects real-world conditions
Plan for production from day one
Package models behind stable APIs, monitor accuracy and drift after launch, and schedule retraining. MLOps practices turn a one-off experiment into a reliable service.
Keep humans in the loop
For high-impact decisions, let people review low-confidence predictions. It builds trust and produces the labelled data that improves the next version.
