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Machines need ‘on the job training’ too

Operationalizing 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.

Matching the right data set to the right production stage is critical for successful deployment of machine learning.
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To read the article, see “Operationalizing machine learning in processes,” September 27, 2021.