Reliable
AI/ML

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Mission Ready Software understands unique reliability issues with AI/ML

Requirements and Data

Establish the AI/ML Goals: Define the problem scope, desired outcomes, and performance metrics.
Consider Bias Factors: Identify potential biases in datasets and ensure fairness.
Select the Right Model: Choose between classical ML models, deep learning, or hybrid approaches based on problem complexity.

Data Collection & Cleaning:
- Ensure diversity in datasets to minimize bias.
- Handle missing, duplicate, or inconsistent data points.
Data Versioning:
- Track dataset versions
Data Augmentation & Synthetic Data:
- Generate additional data to improve model generalization.

Modeling and Testing

Feature Engineering: Identify and optimize key features for better predictions.

Cross-Validation: Use k-fold validation to avoid overfitting and improve generalization.

Data & Model Testing:
- Test data integrity, quality, and bias.
- Compare multiple model architectures to find the best-performing one.
Robustness Testing:
- Check adversarial robustness against manipulated inputs.
- Usestress testing to analyze performance in extreme conditions.
Performance Monitoring:
- Track real-world performance with metrics like precision, recall, F1-score, and drift detection.

Deployment

CI/CD for ML (MLOps):
- Automate training, testing, and deployment
Logging & Monitoring:
- Log errors, data drift, and concept drift

Real-World Monitoring: Continuously monitor deployed models for data drift and performance degradation.
Retraining Pipelines: Automate periodic model retraining with new data.
User Feedback Integration: Collect feedback to refine model predictions and improve reliability.