The platform helps you focus on solving problems with data science

Automate with AI management platform


Too many hacks, tools, custom code, and people needed to accommodate machine learning tasks: interfacing model with other data sources or data transformations inserts many manual operations in the loop and is hard to maintain and monitor


Pipeline creation & scheduling

Visually assemble tasks to automate your projects in minutes.

  • Why? Many of the tasks in the experimentation phase will need to be automated reliably once the project goes into production.
  • How?’s Pipelines provides a visual editor to manually define workflow, and a scheduler to easily automate recurring tasks.

Built-in ML components (retrain, predict)

Use built-in components to quickly set up batch predictions or retrain automations.

  • Why? Many of the tasks requiring automation will revolve around creating batch predictions or retraining models.
  • How? Prevision’s built-in ML components allow you to create a batch prediction or a retrain pipeline in minutes.
compatibility R Python and Node for experiment

Custom Python components

Code any transformation in your favorite language to be included in existing pipelines.

  • Why? Even if many ML projects can be broken down to the same components, custom, business logic will always be needed for peculiarities in the data or to post-process the predictions.
  • How? allows you to include any custom code in your pipelines and run them in the same way as custom components.

Connectors & Data Sources

Manage your data whether it resides in buckets or in SQL databases.

  • Why? Data, even in a single organization, will reside in many different formats and database technologies.
  • How?’s connectors allow you to create connections to your databases, and save them as dynamic datasets in Data Sources to be used in your pipelines.