The platform helps you focus on solving problems with data science

Deploy with AI management platform


Going from a working model to one in production is where many fail. And if it takes too long to deploy a model, a business may lose interest altogether. 

The whole system can go down every time a model is updated.


deploy in minutes

Deploy in minutes

In a few clicks, make your model available to the world across secure and reliable infrastructure.

  • Why? Your model might work on paper, but deploying it to the real world is the true test of its integrity. To make sure it’s indeed performing and delivering value as expected, you need to make it available as soon as possible to your end users.
  • How? In a few clicks, any model managed by can be deployed as a service using a REST API or as a component in a pipeline to generate batch predictions.

Lifecycle management

Update your models seamlessly.

  • Why? Updating a model is not a question of “if,” but rather of “when.” Even if everything is working as expected, external circumstances will change, new data will come in, or new requirements will be drafted, making model updates essential. In those cases, easily updating without a service break—and rolling back without losses—is essential.
  • How? Every Prevision deployment is versioned, and pushing a new iteration is just a matter of a few clicks, with no service interruption.
deploy lifecycle management
deploy custom shiny and flask apps

Custom Shiny & Flask apps

Quickly create dashboards in Python or R and publish to your users.

  • Why? During the course of a machine learning project, dozens of analyses are created to understand model behavior and share that understanding with your stakeholders.
  • How? provides a simple way to deploy R Shiny,  Dash or any type of Flask application in the same reliable infrastructure as your models.