Technology October 13, 2021Operationalizing machine learning depends on a solid data set that the underlying algorithms can analyze and learn from. To get there, deployments span three sequential environments to train ML models: development, user-acceptance testing, and production. The production environment is generally optimal because it uses real-world data. We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at: McKinsey_Website_Accessibility@mckinsey.com To read the article, see “Operationalizing machine learning in processes,” September 27, 2021.