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Updating ML docs (#129)

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* Updating test matrix for 7.6 + removing oss for now.

* Resolving 7.6.0 docs issues

* Updating ML docs
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12
docs/source/reference/ml.rst
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@ -5,6 +5,18 @@ Machine Learning
================
.. currentmodule:: eland.ml
Machine learning is built into the Elastic Stack and enables users to gain insights into their Elasticsearch data.
There are a wide range of capabilities from identifying in
anomalies in your data, to training and deploying regression or classification models based on Elasticsearch data.
To use the Elastic Stack machine learning features, you must have the appropriate license and at least one machine
learning node in your Elasticsearch cluster. If Elastic Stack security features are enabled, you must also ensure
your users have the necessary privileges.
The fastest way to get started with machine learning features is to start a free 14-day trial of Elasticsearch Service in the cloud.
See https://www.elastic.co/guide/en/machine-learning/current/setup.html and other documentation for more detail.
ExternalMLModel
~~~~~~~~~~~~~~~
.. currentmodule:: eland.ml

8
eland/ml/external_ml_model.py
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@ -30,8 +30,8 @@ from xgboost import XGBRegressor, XGBClassifier
class ExternalMLModel(MLModel):
"""
Put a trained inference model in Elasticsearch based on an external model.
An external model that is transformed and added to Elasticsearch.
Transform and serialize a trained 3rd party model into Elasticsearch.
This model can then be used for inference in the Elastic Stack.
Parameters
----------
@ -152,9 +152,9 @@ class ExternalMLModel(MLModel):
def predict(self, X):
"""
Make a prediction using a trained inference model in Elasticsearch.
Make a prediction using a trained model stored in Elasticsearch.
Parameters for this method are not fully compatible with standard sklearn.predict.
Parameters for this method are not yet fully compatible with standard sklearn.predict.
Parameters
----------