[DOCS] Adds machine learning highlights (#52334)

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Lisa Cawley 2020-02-14 08:51:55 -08:00 committed by GitHub
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@ -30,3 +30,43 @@ This lets {es} skip non-competitive hits, which often improves query speed.
In benchmarking tests, this sped up sorts on `long` fields by 10x.
// end::notable-highlights[]
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[float]
==== Simplifying and operationalizing machine learning
With the release of 7.6 the {stack} delivers an end-to-end {ml} pipeline
providing the path from raw data to building, testing, and deploying {ml} models
in production. Up to this point {ml} in the {stack} had primarily focused on
unsupervised techniques by using sophisticated pattern recognition that builds
time series models used for {anomaly-detect}. With the new {dfanalytics}, you
can now use labelled data to train and test your own models, store those models
as {es} indices, and use {ml-docs}/ml-inference.html[inference] to add predicted
values to the indices based on your trained models.
One packaged model that we are releasing in 7.6 is
{ml-docs}/ml-lang-ident.html[{lang-ident}]. If you have documents or sources
that come in a variety of languages, {lang-ident} can be used to determine the
language of text so you can improve the overall search relevance.
{lang-ident-cap} is a trained model that can provide a prediction of the
language of any text field.
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==== {ccs-cap} in {transforms}
{ref}/transforms.html[{transforms-cap}] can now use {ccs} (CCS) for the source
index. Now you can have separate clusters (for example, project clusters) build
entity-centric or feature indices against a primary cluster.
// end::notable-highlights[]
[float]
=== Learn more
Get more details on these features in the
https://www.elastic.co/blog/elasticsearch-7-6-0-released[{es} 7.6 release blog].
For a complete list of enhancements and other changes, check out the
<<release-notes-7.6.0,{es} 7.6 release notes>>.