Gartner Research

How to Operationalize Machine Learning and Data Science Projects

Published: 03 July 2018

ID: G00333499

Analyst(s): Jim Hare , Erick Brethenoux , Shubhangi Vashisth

Summary

The democratization of machine learning platforms is proliferating analytical assets and models. The challenge now is to deploy and operationalize at scale. Data and analytics leaders must establish operational tactics and strategies to secure and systematically monetize data science efforts.

Table Of Contents
  • Key Challenges

Introduction

Analysis

  • Establish a Close, Ongoing Dialogue With Business Counterparts
  • Establish a Systematic Operationalization Process
    • Operationalization Cycle Functionality
    • Operationalization Cycle Process
    • Release Phase: Testing Models in Business Conditions
    • Activation Phase: Operating Models in Real Business Conditions
  • Monitor, Re-evaluate, Tune and Manage Models on an Ongoing Basis
  • Secure the Help of Nonanalytical Personnel
  • Monitor and Constantly Revalidate the Business Value Delivered by Machine Learning Models in Production

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