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Scale a trained model deployment

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Scale a trained model deployment

with Elastic Stack · Artificial Intelligence Management

Use Elastic Stack: its Elasticsearch Request & Response Specification ml trained model API lets you scale a trained model deployment in a single step (POST /_ml/trained_models/{model_id}/deployment/_update). capabilities.work can help you get signed up and connected.

  • Can I change the number of allocations on a model that's already deployed?
  • How do I turn on adaptive allocations for a running model deployment?
Say it to your AI assistant
  • Scale the deployment of {model_id} to {number_of_allocations} allocations.
  • Enable adaptive allocations {adaptive_allocations} on deployed model {model_id}.
For your developer

POST /_ml/trained_models/{model_id}/deployment/_update in Elasticsearch Request & Response Specification ml trained model API. Full spec on APIs.io.

More you can do with Elastic Stack

Get ML memory usage across all nodesGet ML memory usage for one nodeGet machine learning defaults and limitsToggle ML upgrade modeSync ML saved objects in the default spaceChange which spaces ML jobs belong toChange which spaces trained models belong toClear a trained model deployment's inference cacheGet trained model configs by IDUpload a trained modelDelete an unreferenced trained modelCreate or reassign a trained model alias