![]() They also provide critical insights for managing the potential risks of model-based decision making, even as underlying business and technical conditions change. ![]() Model Ops (aka ML Ops) ensures that models continue to deliver value to the organization. The four main steps in the Model Ops process - build, manage, deploy/integrate, and monitor - form a repeatable cycle that you can leverage to reuse your models as software artifacts. Model Ops is the process of operationalizing data science by getting data science models into production and then managing them. Model Operations, or Model Ops, is the answer. You wouldn’t spend all this time and money on creating ML models without putting them into production, would you? You need your models infused into the business so they can help make crucial decisions. Just as you wouldn’t train athletes and not have them compete, the same can be said about data science & machine learning (ML).
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