Intermediate

Testing ML Models

Learn how to build comprehensive test suites for machine learning models, from unit tests on individual components to full model performance validation and regression testing.

Unit Testing Model Components

Break your model into testable components. Each preprocessing step, feature transformation, and post-processing function should have its own unit tests. Test that your model architecture instantiates correctly, that forward passes produce outputs of the expected shape, and that gradients flow properly during training.

✅
Best Practice: Set random seeds in your test fixtures to make model tests as reproducible as possible. While perfect determinism is hard to guarantee (especially on GPUs), seeding eliminates one major source of variability.

Performance Benchmarking

Metric Type Examples When to Use
Classification Accuracy, Precision, Recall, F1, AUC-ROC Binary and multi-class classification tasks
Regression MAE, MSE, RMSE, R-squared Continuous value prediction tasks
NLP BLEU, ROUGE, Perplexity, BERTScore Text generation, translation, summarization
Latency P50, P95, P99 inference time Production serving requirements

Regression Testing

  1. Establish Baselines

    Record performance metrics for your current model version on a fixed evaluation dataset. Store these baselines in version control alongside your code.

  2. Define Thresholds

    Set minimum acceptable performance levels. A new model version must meet or exceed these thresholds before it can be deployed.

  3. Automate Comparisons

    Build CI/CD pipelines that automatically train, evaluate, and compare new model versions against baselines. Fail the build if performance drops below thresholds.

  4. Track Over Time

    Maintain a history of model performance across versions. Visualize trends to catch gradual degradation that might not trigger threshold alerts.

A/B Testing for Models

Shadow Mode

Run the new model alongside the production model, logging predictions without serving them. Compare outputs offline to validate before switching traffic.

Canary Deployment

Route a small percentage of traffic to the new model. Monitor metrics closely and gradually increase traffic if performance is satisfactory.

Interleaving

Mix results from both models in a single response (common in ranking/recommendation). Measure user engagement to determine the better model.

Multi-Armed Bandit

Dynamically allocate traffic to the best-performing model variant, balancing exploration of new models with exploitation of proven ones.

💡
Looking Ahead: In the next lesson, we will explore data testing — how to validate your training data, detect distribution shifts, and ensure data pipeline integrity.