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[8.4][DOCS] Adds ML-related release highlights (#89078)
Co-authored-by: Lisa Cawley <[email protected]>
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docs/changelog/83055.yaml

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@@ -3,3 +3,14 @@ summary: New `frequent_items` aggregation
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area: Machine Learning
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type: enhancement
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issues: []
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highlight:
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title: Frequent itemset aggregation
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body: |-
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The
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{ref}/search-aggregations-bucket-frequent-items-aggregation.html[frequent items aggregation]
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is a new bucket aggregation which identifies items that often occur together.
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It is a form of association rules mining that helps to discover relationships
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between different data points. For example, the aggregation can find log
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events that tend to co-occur and may give a more informative explanation for
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the possible causes of a spike in the logs.
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notable: true

docs/changelog/87361.yaml

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@@ -3,4 +3,17 @@ summary: "Implement per-transform num_failure_retries setting"
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area: Transform
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type: enhancement
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issues: []
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highlight:
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title: Infinite and adaptive retries for transforms
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body: |-
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Infinite and adaptive retries – available in 8.4 – makes it possible for
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transforms to recover after a failure without any user intervention. Retries
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can be configured per transform. The transform retries become less frequent
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progressively. The interval between retries doubles after reaching a one-hour
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threshold. This is because the possibility that retries solve the problem is
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less likely after each failed retry.
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In the *Transforms* page in *{stack-manage-app}* in {kib}, the number of retries
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can be configured when creating a new transform or editing an existing one.
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notable: true
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docs/changelog/88589.yaml

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pr: 88589
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summary: Make composite aggs in datafeeds Generally Available
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area: Machine Learning
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type: feature
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issues: []
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highlight:
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title: Composite aggregations in datafeeds are Generally Available
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body: |-
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The support for
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{ml-docs}/ml-configuring-aggregation.html#aggs-using-composite[composite aggregations]
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in datafeeds is now generally available.
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[discrete]
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[[early-stopping-dfa]]
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=== Early stopping for data frame analytics
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Data frame analytics is even faster in 8.4. The new function automatically
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stops the process of hyperparameter optimization early in case of the
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accuracy gain for a different set of hyperparameter values would be
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insignificant. The early stopping of the optimization process results in a
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shorter runtime for the data frame analytics job.
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notable: true

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