vvipgupta

Generally, MLOps helps in reducing engineering headaches. During our user interviews and customer calls, we realized very early that customization is key for ML model monitoring since all models are different. Thus, we have built the framework to lessen the engineering headache while allowing customizability (think PyTorch). Would love to know your thoughts on this.

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

Just curious, what kind of use cases do you have in mind?

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.

Thanks for the suggestion and links. Completely agree, ML production data management can be painful and to support model refinement for users that operate at scale, an abstraction at the data layer would be a useful feature.

This would be a good application of chatGPT, testing a test for whether it tests for rote learning or fundamentals.