CI/CD for ML pipelines in ML Python - Model Metrics & Evaluation

Metrics & Evaluation - CI/CD for ML pipelines

Which metric matters for CI/CD in ML pipelines and WHY

In CI/CD for ML pipelines, the key metrics are model performance metrics like accuracy, precision, recall, and F1 score, plus pipeline reliability metrics such as build success rate and deployment frequency. These metrics matter because they show if the model is improving and if the pipeline runs smoothly without errors. Monitoring these helps catch problems early and keep the ML system working well.

Confusion matrix example for model evaluation in CI/CD

      Actual \ Predicted | Positive | Negative
      -------------------|----------|---------
      Positive           |    80    |   20
      Negative           |    10    |   90
    

This confusion matrix helps calculate precision, recall, and accuracy to decide if the model is good enough to deploy.

Precision vs Recall tradeoff in ML pipeline deployment

Imagine a spam filter model in your pipeline:

• High precision means few good emails are wrongly marked as spam. This avoids annoying users.
• High recall means most spam emails are caught. This keeps inboxes clean.

Depending on your goal, you might prioritize one over the other before deploying the model.

Good vs Bad metric values for CI/CD in ML pipelines

• Good: Accuracy above 90%, precision and recall balanced above 85%, pipeline build success rate near 100%, fast deployment times.
• Bad: Accuracy below 70%, precision or recall very low (below 50%), frequent pipeline failures, slow or manual deployments.

Common pitfalls in metrics for ML CI/CD pipelines

• Accuracy paradox: High accuracy but poor recall on rare classes can hide problems.
• Data leakage: Training on future data inflates metrics, causing bad deployments.
• Overfitting: Great training metrics but poor real-world results mean the model won’t generalize.
• Ignoring pipeline failures: Deploying models without checking pipeline health can cause downtime.

Self-check question

Your ML pipeline shows 98% accuracy but only 12% recall on fraud detection. Is this model good for production? Why or why not?

Answer: No, it is not good. The low recall means the model misses most fraud cases, which is critical to catch. High accuracy is misleading because fraud is rare, so the model mostly predicts non-fraud correctly but fails where it matters.

Key Result

In CI/CD for ML pipelines, balancing model performance metrics like precision and recall with pipeline reliability metrics ensures safe and effective deployments.