After you build, train, and evaluate your machine learning (ML) model to ensure it’s solving the intended business problem proposed, you want to deploy that model to enable decision-making in business operations. Models that support business-critical functions are deployed to a production environment where a model release strategy is put in place. Given the nature of ML models, where the data is continuously changing, you also want to ensure that a deployed model is still relevant to new data and that the model is updated when this is not the case. This includes choosing a deployment strategy that minimizes risks and downtime. This optimal deployment strategy should maintain high availability of the model, consider the business cost of deploying an inferior model to what is already in production, and contain functionality to easily roll back to a previous model version. Many of these recommended considerations and deployment patterns are also

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