Enhance model performance with data driven analytics and Insight

Enhance model performance with data driven analytics and Insight

There could be various conditions impacting your model performance. If you know those reasons accurately, the model can be further trained with those conditions to perform well.


One of the major challenges faced by data scientists is to understand in what condition models perform poorly. Performance optimization becomes easier if underlying conditions can be presented with subsequent data and visual interpretation on top of that.


Model performance evaluation and identification of issues where the model performs poorly. We generated analytics and insight that helped the AI technology company to retrain the model under certain conditions to increase their health score.
Outcome of our model observability were
  • Health score
  • Generating list of issues based on set threshold for certain rules like F1 score, precision, recall etc
  • Confusion matrix
  • Performance metrics and anomaly counts visualisation


With our model observability reports, data scientists can clearly analyse the issues and conditions. Further, training data prepared for those conditions and models were retrained with a new set of data. This improved the model performance.