DataRobot

What is DataRobot ?

DataRobot has developed a unified, collaborative platform that accelerates the use of AI. The solution allows both data experts and coding novices to create their own predictive machine learning models.

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DataRobot offers JEMS

DataRobot within the JEMS offering

The platform accelerates and democratizes data science through end-to-end automation, from data to value. JEMS has developed its data science expertise in the three offerings of the DataRobot platform, namely:

DataPrep : A collaborative, interactive, and visual tool dedicated to big data, designed to prepare data for AI and feed its predictive model

Auto ML : Automates model creation and training

MLOPsMonitoring and management of created models (regardless of their source - external or from DataRobot)

The platform DataRobot is the scaling engine for your artificial intelligence heritage. As an expert partner, JEMS uses DataRobot to automate complex data science steps without compromising scientific rigour. We enable your teams to quickly identify the best algorithms, reduce Time-to-Value, and guarantee a Trusted AI, transparent and auditable at every stage of the life cycle.

This allows us to meet the needs of our customers, such as:

  • CDO: I need to increase the productivity of my Data-Scientists
  • DPO: We must have a development ethic and detect potential deviations in our AI models
  • CTO: I need to improve data governance

The service offer

Data Ingineering

DataPrep: A collaborative, interactive, and visual tool dedicated to big data, designed to prepare data for AI and feed its predictive model.

  • Automate DataPrep.
  • Improve TCO.
  • Reorient DS/BA tasks towards high VA tasks.

 

Data Science

Auto ML: will enable the automation of model creation and training.

  • Increase the productivity of data science activities (beyond DS, BAs can automate certain tasks, reducing dependency).
  • Reduction in time to market.

 

Dev / IT

MLOps: Monitoring and management of created models (regardless of their source—outside & from DataRobot)

  • Putting a model into production—decision support.
  • Ensuring ethics, performance, and detecting model drift.
  • Improving governance.

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