When a model gets deployed to a production environment, inference speed matters. Models with fast inference speeds require less resources to run, which translates to cost savings, and applications that consume the models’ predictions benefit from the improved performance.
For example, let’s say your website uses a regression model to predict mortgage rates for aspiring home buyers to see what type of rate they could expect, based on inputs they provide such as the size of the down payment, their loan term, and the county in which they’re looking to buy. A model that can send a prediction back in 10 milliseconds versus 200 milliseconds for every time an input is updated makes a massive difference in terms of the website’s responsiveness and user experience.
Amazon SageMaker Neo allows you to unlock such performance improvements and cost savings in a matter of minutes. It does this by compiling models into

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