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Updating ML docs (#129)
* Updating test matrix for 7.6 + removing oss for now. * Resolving 7.6.0 docs issues * Updating ML docs
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@ -5,6 +5,18 @@ Machine Learning
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.. currentmodule:: eland.ml
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.. currentmodule:: eland.ml
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Machine learning is built into the Elastic Stack and enables users to gain insights into their Elasticsearch data.
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There are a wide range of capabilities from identifying in
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anomalies in your data, to training and deploying regression or classification models based on Elasticsearch data.
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To use the Elastic Stack machine learning features, you must have the appropriate license and at least one machine
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learning node in your Elasticsearch cluster. If Elastic Stack security features are enabled, you must also ensure
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your users have the necessary privileges.
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The fastest way to get started with machine learning features is to start a free 14-day trial of Elasticsearch Service in the cloud.
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See https://www.elastic.co/guide/en/machine-learning/current/setup.html and other documentation for more detail.
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ExternalMLModel
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ExternalMLModel
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~~~~~~~~~~~~~~~
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~~~~~~~~~~~~~~~
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.. currentmodule:: eland.ml
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.. currentmodule:: eland.ml
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@ -30,8 +30,8 @@ from xgboost import XGBRegressor, XGBClassifier
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class ExternalMLModel(MLModel):
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class ExternalMLModel(MLModel):
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"""
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"""
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Put a trained inference model in Elasticsearch based on an external model.
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Transform and serialize a trained 3rd party model into Elasticsearch.
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An external model that is transformed and added to Elasticsearch.
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This model can then be used for inference in the Elastic Stack.
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Parameters
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Parameters
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----------
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@ -152,9 +152,9 @@ class ExternalMLModel(MLModel):
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def predict(self, X):
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def predict(self, X):
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"""
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"""
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Make a prediction using a trained inference model in Elasticsearch.
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Make a prediction using a trained model stored in Elasticsearch.
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Parameters for this method are not fully compatible with standard sklearn.predict.
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Parameters for this method are not yet fully compatible with standard sklearn.predict.
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Parameters
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Parameters
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----------
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