Model evaluation is a critical step in the ML lifecycle, but many enterprises struggle with it. This post highlights the most common mistakes—like using the wrong metrics, ignoring data drift, and lacking reproducibility—and offers strategies to ensure your evaluations are meaningful and actionable.

Common Pitfalls

• Wrong Metrics: Relying on accuracy alone can be misleading. Precision, recall, F1, and AUC are often more informative.
• Ignoring Data Drift: Failing to monitor for changes in input data can lead to silent model degradation.
• Lack of Reproducibility: Not tracking code, data, and environment versions makes it hard to reproduce results.
• Overfitting: Evaluating only on training data or not using proper validation splits.

How to Avoid These Failures

1. Use Multiple Metrics: Evaluate models from different angles to get a complete picture.
2. Monitor Continuously: Set up systems to detect drift and performance drops in production.
3. Track Everything: Use tools like MLflow or DVC to log experiments, data, and code versions.
4. Validate Properly: Always use holdout sets and cross-validation.

By addressing these pitfalls, enterprises can build more reliable, trustworthy, and effective AI systems.