What do people use NStack for?
Productionise your models in the cloud without complex engineering, where they can be used in workflows and attached to data-sources. For instance, you can build a Random Forest classifier locally in Python, publish it to your cloud provider, and connect it to a streaming system, database, data-lake, or HTTP endpoint in under 10 minutes.
Transform disparate and disconnected data-sources -- such as 3rd-party APIs, legacy infrastructure, or databases into streams of typed, structured records, which can be composed together. For instance, you could set up a workflow in the cloud which pipes the Twitter Ads API into your data-lake (and even do some modelling in Python in-transit) in under 5 minutes.