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.

Awesome! Big fan of OS -- arize is powerful yet expensive, so I think there's a big market there. Alerting is super tough to get right, and false positives are often worse than no alerting at all. In ML its even harder cause "data looks weird" is like 90% of the bugs.

Anyway, congrats! Excited to see where you go with this.

Thanks! Also, wondering how did you hear about Arize? Have you dealt with the pain of ML model monitoring in the past?

Yeah, so we used it and built some custom solutions at Stitch Fix. Reach out to my co-founder Stefan (also in YC '23) -- he'll have some insight for you.

Thanks! Reaching out to Stefan