Additionally, refinement is a key focus of ours. Figuring out the best data points to retrain the model upon has twin benefits:
1) It provides automated issue resolution and saves data scientists' effort to debug and fix their models.
2) It allows us to reduce false positives in alerting: we send alerts only when we see a dip in model performance, or retraining can lead to improved model accuracy.
1) It provides automated issue resolution and saves data scientists' effort to debug and fix their models. 2) It allows us to reduce false positives in alerting: we send alerts only when we see a dip in model performance, or retraining can lead to improved model accuracy.