Production-level pipeline for your data science

Harness the power of algorithms with a frictionless deployment process

Dataset exploration at your fingertips

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    Explore

    Instant exploration through Jupyter notebook instances hosted on ForePaaS

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    Integrate

    Connect with other components to export existing scripts as custom data processing operations

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    Scale

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

53%

of data science projects never get fully deployed

Source: Gartner Research 2018-2019

36%

of data scientists report dirty data as their main challenge

Source: Kaggle 2017 State of Data Science

4%

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

Source: Crowdflower 2016 Data Science Report

Model management ready-made for deployment

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    Deployment-ready

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

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    Open

    Use popular frameworks to set up production-grade environments with all requirements met

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    Comprehensive

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

Our ML killer features

Marketplace

Open and integrated with frameworks and ML-standard best practices

Mass-production

Deploy immediately on a flexible and scalable infrastructure

One environment

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

Watch the Machine Learning Manager Demo

Ready to get started?

Contact us