Key considerations for operationalizing machine learning
Training a machine learning model is important, but you need to get the model into a production environment working on real-world data to get real-world value from it. In the lingo of artificial intelligence, putting machine learning models into real-world environments where they are acting on real-world data and providing real-world predictions is called “operationalizing” the machine learning models.
Why don’t we simply say we’re “deploying” an AI model or putting it into production? There’s a simple reason for the terminology difference. Once a model has been trained, it needs to be applied to a particular problem, but you can apply that model in any of a number of ways. The model can sit on a desktop machine providing results on demand, or it can sit on the edge in a mobile device, or it can sit in a cloud or server environment providing results in a wide range of use cases.
Each one of these places where the model operates can be considered a separate deployment, so simply saying the model is deployed doesn’t give us enough information. This terminology difference is not the only thing that is substantially different when operationalizing machine learning models.
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