Scalable AI starts with storage: Guide to model artifact strategies
Managing large model artifacts is a common bottleneck in MLOps. Baking models into container images leads to slow, monolithic deployments, and downloading them at startup introduces significant delays. This guide explores a better way: decoupling your models from your code by hosting them in Cloud Storage and accessing them efficiently from GKE and Cloud Run. […]
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