Deploy data-science models to the cloud and build workflows with databases, APIs, and more.

Deploy your models to the cloud in under a minute.

See example

Use your models in streaming workflows with other systems, such as the data warehouse, APIs, or file stores.

Run your workflows on your company's cloud in one command.

Connect code with your company's event and data-sources to build data-driven workflows. NStack handles all infrastructure: you run more experiments more quickly, and self-serve on your cloud-provider.

What do people use NStack for?

Productionising Models

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.

Data Integration

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.