For the past few years, enterprise leaders have focused on standing up AI quickly. This means getting the hardware, models and datasets needed to enable new use cases and drive new business value. Now, AI is no longer just a proof of concept. Many businesses have made production AI applications a load-bearing part of their operations, creating new dependencies in the process. They need to shift their priorities accordingly: Instead of just deploying AI quickly, they also need to keep it running. They need to answer key questions like: How long can we run without our primary model? If we needed to switch…

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