Machine Learning testing strategies

Testing Machine Learning Models Model Accuracy Using special test data to see how well a model works. Measuring results with scores like precision, recall, and accuracy. Robustness Using tools like Great Expectations to check the quality of data. Making sure models work well even with tricky or unusual inputs. Bias and Fairness Using tools like IBM AIF360, Google What-If, and Microsoft Fairlearn to find and fix unfairness in models. Integration Testing how applications work with systems like EHR (Electronic Health Records). Ensuring smooth data sharing and system compatibility. Monitoring Keeping an eye on how models behave over time. Using tools like Amazon SageMaker Model Monitor to spot problems. Regulatory Compliance Making sure AI follows important rules like HIPAA and protects personal data. Keeping sensitive information safe with strong security measures.

May 13, 2025 - 03:39

Model Accuracy
- Using special test data to see how well a model works.
- Measuring results with scores like precision, recall, and accuracy.
Robustness
- Using tools like Great Expectations to check the quality of data.
- Making sure models work well even with tricky or unusual inputs.
Bias and Fairness
- Using tools like IBM AIF360, Google What-If, and Microsoft Fairlearn to find and fix unfairness in models.
Integration
- Testing how applications work with systems like EHR (Electronic Health Records).
- Ensuring smooth data sharing and system compatibility.
Monitoring
- Keeping an eye on how models behave over time.
- Using tools like Amazon SageMaker Model Monitor to spot problems.