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Kubernetes for ML: Deploying Models at Scale
Kubernetes runs copies of your model service, replaces the ones that die and swaps versions without downtime, if the manifest is right. This guide deploys a concrete-strength model to a local cluster, kills a pod, rolls out a new version under continuous traffic with no failed requests, and rolls back.
Deploying a Gen AI App on the Cloud: A Walkthrough
A notebook that calls a model is not an application. This walkthrough turns one into a small web service with input limits, configuration from the environment, friendly failure handling and fast tests, then packages it in a container and deploys it to Google Cloud Run with the model key held as a secret.
Deploying ML Models on AWS, Azure and GCP
AWS, Azure and Google Cloud all host models behind managed endpoints, but each expects a different serving contract. This guide runs one real model behind all three contracts locally, including Microsoft's own inference server, and shows what changes between clouds and what should stay exactly the same.