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We take a proactive, full lifecycle approach - from data to deployment to ongoing operations. We will provide reports to key stakeholders on model performance trends and recommended actions to optimize the AI system over time.
We take a proactive, full lifecycle approach - from data to deployment to ongoing operations. We will provide reports to key stakeholders on model performance trends and recommended actions to optimize the AI system over time.
Data Quality Analysis
Data Quality Analysis
Assess the quality of the data being used to train and test the AI model. Look for issues like bias, errors, and inconsistencies that could impact model performance.
Model Testing & Validation
Model Testing & Validation
We assist you in developing testing protocols and benchmarks to evaluate the AI model's performance over time. These tests would cover accuracy, precision/recall, robustness, and other relevant metrics. Validation ensures the model works as intended.
Monitoring and Alerting
Monitoring and Alerting
We will set up monitoring on key performance metrics and configure alerts if thresholds are exceeded. This allows for proactive detection of model degradation.
Root Cause Analysis
Root Cause Analysis
We will Investigate declines in model performance to determine the root cause. Issues could be data drift, concept drift, and model staleness.
Model Re-Training & Updates
Model Re-Training & Updates
If necessary, we will retrain or fine-tune the model on new data to improve performance. Retraining frequency will depend on model degradation rates.
Model Governance
Model Governance
We will establish model risk management protocols, model ops procedures, and controls to ensure rigorous AI governance and compliance.
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