ML Model Testing is a critical phase that evaluates whether an ML model meets functional and non-functional performance criteria. It focuses on ensuring that the model operates accurately and reliably in real-world scenarios.
Key Aspects:
- Functional Testing: Validates that the model produces correct outputs based on input data.
- Performance Testing: Assesses how well the model performs under different conditions, such as load and response time.
- Robustness Testing: Evaluates how the model handles unexpected inputs or changes in data distribution.
- Bias Testing: Identifies and mitigates biases in the model’s predictions to ensure fairness.
Effective ML model testing helps enhance accuracy, reliability and overall performance, making it essential for successful deployment.