[DOCS] Removes beta labels from DFA docs. (elastic#1625)

szabosteve · web-flow · commit 03dc8c634768 · 2021-03-29T11:33:11.000+02:00
diff --git a/docs/en/stack/ml/df-analytics/dfa-classification.asciidoc b/docs/en/stack/ml/df-analytics/dfa-classification.asciidoc
@@ -2,7 +2,6 @@
 [[dfa-classification]]
 = {classification-cap}
 
-beta::[]
 
 {classification-cap} is a {ml} process that enables you to predict the class or
 category of a data point in your data set. Typical examples of {classification}
diff --git a/docs/en/stack/ml/df-analytics/dfa-outlier-detection.asciidoc b/docs/en/stack/ml/df-analytics/dfa-outlier-detection.asciidoc
@@ -2,7 +2,6 @@
 [[dfa-outlier-detection]]
 = {oldetection-cap}
 
-beta::[]
 
 {oldetection-cap} is an analysis for identifying data points (outliers) whose 
 feature values are different from those of the normal data points in a 
diff --git a/docs/en/stack/ml/df-analytics/dfa-regression.asciidoc b/docs/en/stack/ml/df-analytics/dfa-regression.asciidoc
@@ -2,7 +2,6 @@
 [[dfa-regression]]
 = {regression-cap}
 
-beta::[]
 
 {reganalysis-cap} is a {ml} process for estimating the relationships among 
 different fields in your data, then making further predictions based on these 
diff --git a/docs/en/stack/ml/df-analytics/dfanalytics-examples.asciidoc b/docs/en/stack/ml/df-analytics/dfanalytics-examples.asciidoc
@@ -6,7 +6,6 @@
 <titleabbrev>Examples</titleabbrev>
 ++++
 
-beta::[]
 
 These examples demonstrate how to use {dfanalytics} to derive useful insights 
 from your data.
diff --git a/docs/en/stack/ml/df-analytics/ecommerce-outliers.asciidoc b/docs/en/stack/ml/df-analytics/ecommerce-outliers.asciidoc
@@ -3,7 +3,6 @@
 [[ecommerce-outliers]]
 = Finding outliers in the eCommerce sample data
 
-beta::[]
 
 The goal of <<dfa-outlier-detection,{oldetection}>> is to find the most unusual
 documents in an index. Let's try to detect unusual customer behavior in the 
diff --git a/docs/en/stack/ml/df-analytics/flightdata-classification.asciidoc b/docs/en/stack/ml/df-analytics/flightdata-classification.asciidoc
@@ -3,7 +3,6 @@
 [[flightdata-classification]]
 = Predicting delayed flights with {classanalysis}
 
-beta::[]
 
 Let's try to predict whether a flight will be delayed or not by using the 
 {kibana-ref}/add-sample-data.html[sample flight data]. The data set contains 
diff --git a/docs/en/stack/ml/df-analytics/flightdata-regression.asciidoc b/docs/en/stack/ml/df-analytics/flightdata-regression.asciidoc
@@ -3,7 +3,6 @@
 [[flightdata-regression]]
 = Predicting flight delays with {reganalysis}
 
-beta::[]
 
 Let's try to predict flight delays by using the 
 {kibana-ref}/add-sample-data.html[sample flight data]. The data set contains
diff --git a/docs/en/stack/ml/df-analytics/hyperparameters.asciidoc b/docs/en/stack/ml/df-analytics/hyperparameters.asciidoc
@@ -2,7 +2,6 @@
 [[hyperparameters]]
 = Hyperparameter optimization
 
-beta::[]
 
 When you create a {dfanalytics-job} for {classification} or {reganalysis}, there
 are advanced configuration options known as _hyperparameters_. The ideal
diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-feature-processors.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-feature-processors.asciidoc
@@ -5,7 +5,6 @@
 <titleabbrev>Feature processors</titleabbrev>
 ++++
 
-beta::[]
 
 {dfanalytics-cap} automatically includes a <<ml-feature-encoding>> phase, which 
 transforms categorical features into numerical ones. If you want to have more 
diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-limitations.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-limitations.asciidoc
@@ -6,7 +6,6 @@
 <titleabbrev>Limitations</titleabbrev>
 ++++
 
-beta::[]
 
 The following limitations and known problems apply to the {version} release of 
 the Elastic {dfanalytics} feature. The limitations are grouped into four 
diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-overview.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-overview.asciidoc
@@ -2,7 +2,6 @@
 [[ml-dfa-overview]]
 = Overview
 
-beta::[]
 
 {dfanalytics-cap} enable you to perform different analyses of your data and 
 annotate it with the results. By doing this, it provides additional insights 
diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-phases.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-phases.asciidoc
@@ -5,7 +5,6 @@
 <titleabbrev>How it works</titleabbrev>
 ++++
 
-beta::[]
 
 A {dfanalytics-job} is essentially a persistent {es} task. During its life 
 cycle, it goes through four or five main phases depending on the analysis type:
diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-scale.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-scale.asciidoc
@@ -2,7 +2,6 @@
 [[ml-dfa-scale]]
 = Working with {dfanalytics} at scale
 
-beta::[]
 
 A {dfanalytics-job} has numerous configuration options. Some of them may have a 
 significant effect on the time taken to train a model. The training time depends 
diff --git a/docs/en/stack/ml/df-analytics/ml-dfanalytics-evaluate.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfanalytics-evaluate.asciidoc
@@ -2,7 +2,6 @@
 [[ml-dfanalytics-evaluate]]
 = Evaluating {dfanalytics}
 
-beta::[]
 
 Using the {dfanalytics} features to gain insights from a data set is an 
 iterative process. You might need to experiment with different analyses, 
diff --git a/docs/en/stack/ml/df-analytics/ml-dfanalytics.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfanalytics.asciidoc
@@ -11,7 +11,6 @@ dimensional "tabular" data structure, in other words a {dataframe}.
 {ref}/transforms.html[{transforms-cap}] enable you to create 
 {dataframes} which can be used as the source for {dfanalytics}.
 
-beta::[]
 
 {dfanalytics-cap} enable you to perform different analyses of your data and 
 annotate it with the results. Consult <<setup>> to learn more about the licence 
diff --git a/docs/en/stack/ml/df-analytics/ml-feature-encoding.asciidoc b/docs/en/stack/ml/df-analytics/ml-feature-encoding.asciidoc
@@ -2,7 +2,6 @@
 [[ml-feature-encoding]]
 = Feature encoding
 
-beta::[]
 
 {ml-cap} models can only work with numerical values. For this reason, it is 
 necessary to transform the categorical values of the relevant features into 
diff --git a/docs/en/stack/ml/df-analytics/ml-feature-importance.asciidoc b/docs/en/stack/ml/df-analytics/ml-feature-importance.asciidoc
@@ -2,7 +2,6 @@
 [[ml-feature-importance]]
 = {feat-imp-cap}
 
-beta::[]
 
 {feat-imp-cap} values indicate which fields had the biggest impact on each 
 prediction that is generated by {classification} or {regression} analysis. Each
diff --git a/docs/en/stack/ml/df-analytics/ml-inference.asciidoc b/docs/en/stack/ml/df-analytics/ml-inference.asciidoc
@@ -2,7 +2,6 @@
 [[ml-inference]]
 = {infer-cap}
 
-beta::[]
 
 {infer-cap} is a {ml} feature that enables you to use supervised {ml} processes 
 – like <<dfa-regression>> or <<dfa-classification>> – not only as a batch 
diff --git a/docs/en/stack/ml/df-analytics/ml-lang-ident.asciidoc b/docs/en/stack/ml/df-analytics/ml-lang-ident.asciidoc
@@ -2,7 +2,6 @@
 [[ml-lang-ident]]
 = {lang-ident-cap}
 
-beta::[]
 
 {lang-ident-cap} is a trained model that you can use to determine the language
 of text. You can reference the {lang-ident} model in an
diff --git a/docs/en/stack/ml/df-analytics/ml-supervised-workflow.asciidoc b/docs/en/stack/ml/df-analytics/ml-supervised-workflow.asciidoc
@@ -2,7 +2,6 @@
 [[ml-supervised-workflow]]
 = Introduction to supervised learning
 
-beta::[]
 
 Elastic supervised learning enables you to train a {ml} model based on training 
 examples that you provide. You can then use your model to make predictions on 
diff --git a/docs/en/stack/ml/df-analytics/ml-trained-models.asciidoc b/docs/en/stack/ml/df-analytics/ml-trained-models.asciidoc
@@ -2,7 +2,6 @@
 [[ml-trained-models]]
 = Trained models
 
-beta::[]
 
 When you use a {dfanalytics-job} to perform {classification} or {reganalysis},
 it creates a {ml} model that is trained and tested against a labelled data set.
