[7.x] [ML] adds new trained model alias API to simplify trained model updates and deployments (#68922) #69208
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…es and deployments (elastic#68922) A `model_alias` allows trained models to be referred by a user defined moniker. This not only improves the readability and simplicity of numerous API calls, but it allows for simpler deployment and upgrade procedures for trained models. Previously, if you referenced a model ID directly within an ingest pipeline, when you have a new model that performs better than an earlier referenced model, you have to update the pipeline itself. If this model was used in numerous pipelines, ALL those pipelines would have to be updated. When using a `model_alias` in an ingest pipeline, only that `model_alias` needs to be updated. Then, the underlying referenced model will change in place for all ingest pipelines automatically. An additional benefit is that the model referenced is not changed until it is fully loaded into cache, this way throughput is not hampered by changing models.
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Pinging @elastic/ml-core (Team:ML) |
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A
model_aliasallows trained models to be referred by a user defined moniker.This not only improves the readability and simplicity of numerous API calls, but it allows for simpler deployment and upgrade procedures for trained models.
Previously, if you referenced a model ID directly within an ingest pipeline, when you have a new model that performs better than an earlier referenced model, you have to update the pipeline itself. If this model was used in numerous pipelines, ALL those pipelines would have to be updated.
When using a
model_aliasin an ingest pipeline, only thatmodel_aliasneeds to be updated. Then, the underlying referenced model will change in place for all ingest pipelines automatically.An additional benefit is that the model referenced is not changed until it is fully loaded into cache, this way throughput is not hampered by changing models.