Production-level pipeline for your data science

Harness the power of algorithms with a frictionless deployment process

Data exploration at your fingertips

  • Explore

    Instant exploration through our notebook built on Apache Zeppelin with pre-loaded packages

  • Integrate

    Simple integration with components for exporting paragraphs as custom ETL actions

  • Scale

    Scale up or down without redeployment or waiting time for your notebook


of data science projects never get fully deployed

Source: Gartner Research 2018-2019


of data scientists report dirty data as their main challenge

Source: Kaggle 2017 State of Data Science


of a data scientist’s time actually spent on refining algorithms

Source: Crowdflower 2016 Data Science Report

Model management engineered for deployment

  • Deployment-ready

    Models can be pushed as independent APIs linked to the DataPlant’s access rights

  • Open

    Leverage existing work via multiple integrations or use industry-standard frameworks

  • Comprehensive

    Serve the whole value chain from training and feature engineering to scoring

Our ML killer features


Open and integrated with frameworks and ML-standard best practices


Deploy immediately on a flexible and scalable infrastructure

One environment

Manage training and scoring from a user-friendly interface for technical and newbies

Ready to get started?

Join now