Machine Learning testing strategies

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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.

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