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Introduction

DataRobot began as a tool that applied a brute-force approach to automatically testing dozens of machine-learning models side-by-side to identify the most suitable one; it has evolved to address the lifecycle of algorithm creation, culminating with deployment.

Highlights

  • DataRobot offers an end-to-end approach for automating machine-learning problem setup and model evaluation.

Features and Benefits

  • Analyzes how DataRobot automates the machine-learning model development, evaluation, and deployment lifecycle.
  • Analyzes how DataRobot should address the last mile with collaboration to bring business analysts and domain experts into the machine-learning lifecycle.

Key questions answered

  • How does DataRobot differentiate from other tools targeting the machine-learning modeling lifecycle?
  • What are the next steps for DataRobot to secure its foothold, not only with data scientists, but with the rest of the business?

Table of contents

Summary

  • Catalyst
  • Key messages
  • Ovum view

Recommendations for enterprises

  • Why put DataRobot on your radar?

Highlights

  • Background
  • Current position

Data sheet

  • Key facts

Appendix

  • On the Radar
  • Further reading
  • Author

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