I was working on some critical models where we had a lot of model turnover, we were creating and putting aside lots of models, and a time came where we could not even keep a track of all our created models. Our team looked for a model monitoring platform where we could organize, store, and keep an eye on all our models, and hence we started using Datatron. One of the best features of datatron that I've come across is the ModelOps that made deploying and managing AI models quite easier. With the help of this feature, whenever a model was ready, it was quickly sent into the production environment because of the automated platform. Review collected by and hosted on G2.com.

While offering a host of features it is normal to have anyone lagging feature. The parallelization and distributed computing department is a place where the platform lags. Though we had multiple instances connected, the platform could not identify all at once, and due to this certain systems experienced heavy load while some remained idle. This uneven distribution might affect model predictions and performance, and this is something no one would like to happen with them. Review collected by and hosted on G2.com.