Viewing profile — vvipgupta

Thanks for letting us know. We did some docs restructuring before the launch, and missed fixing this link. It is now available here: https://docs.uptrain.ai/docs/uptrain-examples/q…

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 si…

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?

Thanks for the very relevant comment :) We provide users the option to attach their training data from csv/json (working to support loading from cloud storage provider or data lake…

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 …

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 …

Hello, we are Shikha, Sourabh, and Vipul - co-founders at UpTrain, an open-source ML observability toolkit. UpTrain helps you monitor the performance of your machine learning appli…

The same is true for data (aka gradients) consistency while training large ML models. Asynchronous SGD is as good (and maybe even faster) than synchronous SGD: https://papers.nips.…

You might also want to check out https://github.com/lucidrains/PaLM-rlhf-pytorch

When training over multiple GPUs, it's hard not to think about Ray ( https://docs.ray.io/en/latest/train/train.html ). Ray, as an open-source project, has exploded over the last fe…

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