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Deploy a trained model to ML nodes

One step with Elastic Stack. We can help you get access.

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Deploy a trained model to ML nodes

with Elastic Stack · Artificial Intelligence Management

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

  • How do I deploy a trained model so it can serve inference?
  • Can I choose the number of allocations and threads per allocation when starting a deployment?
Say it to your AI assistant
  • Start a deployment of trained model {model_id}.
  • Deploy {model_id} with {number_of_allocations} allocations and {threads_per_allocation} threads each.
For your developer

POST /_ml/trained_models/{model_id}/deployment/_start 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