Production-level pipeline for your data science
Harness the power of algorithms with a frictionless deployment process
Dataset exploration at your fingertips
Explore
Instant exploration through Jupyter notebook instances hosted on ForePaaS
Integrate
Connect with other components to export existing scripts as custom data processing operations
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
Deployment-ready
Models can be pushed as independent APIs linked to the DataPlant’s access rights
Open
Use popular frameworks to set up production-grade environments with all requirements met
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