MLOps at the edge: Running AI models at the edge

In Part 1: Preparing AI models for deployment, we saw how our model left the training pipeline as a signed, optimized Open Container Initiative (OCI) artifact ready to travel to the edge. Now, our next question is: what will be the implications of running those AI workloads at the edge? Running AI inference in a data center means using standardized hardware and abundant memory. At the edge, a single fleet might span multiple hardware platforms.Before thinking about update strategies or…

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