An AI project is more than training a model. Here’s what you can’t skip.
Data¶
- ☐ Data quality verified
- ☐ Bias in data analyzed
- ☐ Train/validation/test split
- ☐ Data versioning (DVC)
- ☐ PII in data handled
Model¶
- ☐ Baseline model (even simple)
- ☐ Experiment tracking (MLflow, W&B)
- ☐ Hyperparameter tuning
- ☐ Model evaluation metrics defined
- ☐ A/B test plan
Deployment¶
- ☐ Model serving infrastructure
- ☐ Model versioning
- ☐ Canary deployment
- ☐ Rollback mechanism
- ☐ Latency and throughput tested
Monitoring¶
- ☐ Data drift detection
- ☐ Model performance monitoring
- ☐ Prediction logging
- ☐ Alerting on degradation
- ☐ Retraining pipeline
Ethics & Compliance¶
- ☐ Fairness metrics
- ☐ Explainability (SHAP, LIME)
- ☐ User consent for AI decisions
- ☐ Human-in-the-loop for critical decisions
- ☐ AI Act compliance (EU)
Reality¶
87% of ML projects never make it to production. Checklists help. But the key is a clear business problem.
aimlproject