the most effective model for the business problem you’re trying to solve. For example, there may be 30 models that could show positive results, and to pick the right one, the automation will run through these models to show you which model or combination of models is most effective based on your specific needs, whether that’s lowering delinquency rates, increasing conversions, reducing risk, lending more, etc. Getting models live and keeping them effective: It’s often much easier to do things in the comfort of your own home/office than it is to make things work in the real world—and AI is no different. Deploying models can be daunting – 47% of executives find it difficult to integrate cognitive projects into existing processes and systems 6 . When/ if they get deployed, performance monitoring is often limited, meaning that when the models drift, the reduction in their effectiveness isn’t noticed or addressed as soon as it should be. This directly impacts their ability to make accurate predictions. To be successful with your AI project you’ll need an MLOps solution that simplifies the deployment, monitoring, and retraining of your models. Again, this is something that you could build internally, but partnering with an external resource is a cost- effective option. And, if you’ve already started on your AI journey but have hit roadblocks with technology, you don’t need to throw away your existing models, instead a technology partner should be able to easily migrate, recreate, or shadow run your models.
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