UNIXMENS Telegram 20384
You’ve trained the model, packaged it on Red Hat OpenShift AI, and it’s ready to work. The next move is exposing it through an API so people and applications can use it. At that moment, your model stops being an internal experiment and becomes a front-door service. And like any front door, somebody is going to knock … sometimes it’s the right user, sometimes not.Your model is no longer just a project in a lab: it’s a production endpoint. And like any endpoint, it’s a target. How do you ensure that only the right applications and users are interacting with it? How do you protect the

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You’ve trained the model, packaged it on Red Hat OpenShift AI, and it’s ready to work. The next move is exposing it through an API so people and applications can use it. At that moment, your model stops being an internal experiment and becomes a front-door service. And like any front door, somebody is going to knock … sometimes it’s the right user, sometimes not.Your model is no longer just a project in a lab: it’s a production endpoint. And like any endpoint, it’s a target. How do you ensure that only the right applications and users are interacting with it? How do you protect the

via Red Hat Blog https://ift.tt/eBUR67a

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