From 8cae9a4542f73e73a651a841a0f721626e98fab8 Mon Sep 17 00:00:00 2001 From: "mergify[bot]" <37929162+mergify[bot]@users.noreply.github.com> Date: Tue, 23 May 2023 11:44:33 -0400 Subject: [PATCH] [DOCS] Add frontmatter for migration (#2402) (#2409) Co-authored-by: Lisa Cawley --- .../anomaly-detection/ml-ad-finding-anomalies.asciidoc | 7 ++++--- .../stack/ml/df-analytics/ml-dfa-classification.asciidoc | 7 ++++--- .../ml/df-analytics/ml-dfa-outlier-detection.asciidoc | 7 ++++--- docs/en/stack/ml/df-analytics/ml-dfa-overview.asciidoc | 9 +++++---- docs/en/stack/ml/df-analytics/ml-dfa-regression.asciidoc | 8 ++++---- docs/en/stack/ml/df-analytics/ml-how-dfa-works.asciidoc | 7 ++++--- docs/en/stack/ml/get-started/ml-getting-started.asciidoc | 8 +++++--- docs/en/stack/ml/machine-learning-intro.asciidoc | 7 +++++-- docs/en/stack/ml/nlp/ml-nlp-classify-text.asciidoc | 9 ++++----- docs/en/stack/ml/nlp/ml-nlp-deploy-models.asciidoc | 7 ++++--- docs/en/stack/ml/nlp/ml-nlp-extract-info.asciidoc | 6 ++++-- docs/en/stack/ml/nlp/ml-nlp-model-ref.asciidoc | 5 +++++ docs/en/stack/ml/nlp/ml-nlp.asciidoc | 6 ++++-- 13 files changed, 56 insertions(+), 37 deletions(-) diff --git a/docs/en/stack/ml/anomaly-detection/ml-ad-finding-anomalies.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-ad-finding-anomalies.asciidoc index 621c3f3d0..dbb1db544 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-ad-finding-anomalies.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-ad-finding-anomalies.asciidoc @@ -4,9 +4,10 @@ Finding anomalies ++++ -:keywords: {ml-init}, {stack}, {anomaly-detect} -:description: An introduction to {ml} {anomaly-detect}, which analyzes time \ -series data to identify and predict anomalous patterns in your data. +:frontmatter-description: An introduction to {ml} {anomaly-detect}, which analyzes time series data to identify and predict anomalous patterns in your data. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [overview] +:frontmatter-tags-user-goals: [analyze] The {ml} {anomaly-detect} features automate the analysis of time series data by creating accurate baselines of normal behavior in your data. These baselines diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-classification.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-classification.asciidoc index 28e5240d4..8dabcc97f 100644 --- a/docs/en/stack/ml/df-analytics/ml-dfa-classification.asciidoc +++ b/docs/en/stack/ml/df-analytics/ml-dfa-classification.asciidoc @@ -3,9 +3,10 @@ [[ml-dfa-classification]] = Predicting classes with {classification} -:keywords: {ml-init}, {stack}, {dfanalytics}, {classification} -:description: An introduction to {ml} {classification}, which enables you to \ -predict classes of data points in a data set. +:frontmatter-description: An introduction to {ml} {classification}, which enables you to predict classes of data points in a data set. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [how-to] +:frontmatter-tags-user-goals: [analyze] {classification-cap} is a {ml} process that predicts the class or category of a data point in a data set. For a simple example, consider how the shapes in the diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-outlier-detection.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-outlier-detection.asciidoc index 1fc2f275b..a658d6f03 100644 --- a/docs/en/stack/ml/df-analytics/ml-dfa-outlier-detection.asciidoc +++ b/docs/en/stack/ml/df-analytics/ml-dfa-outlier-detection.asciidoc @@ -3,9 +3,10 @@ [[ml-dfa-finding-outliers]] = Finding outliers -:keywords: {ml-init}, {stack}, {dfanalytics}, {oldetection} -:description: An introduction to {ml} {oldetection}, which enables you to \ -find unusual data points in a data set compared to the normal data points. +:frontmatter-description: An introduction to {ml} {oldetection}, which enables you to find unusual data points in a data set compared to the normal data points. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [how-to] +:frontmatter-tags-user-goals: [analyze] {oldetection-cap} is identification of data points that are significantly different from other values in the data set. For example, outliers could be diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-overview.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-overview.asciidoc index 97550584e..b99752164 100644 --- a/docs/en/stack/ml/df-analytics/ml-dfa-overview.asciidoc +++ b/docs/en/stack/ml/df-analytics/ml-dfa-overview.asciidoc @@ -5,10 +5,11 @@ ++++ Overview ++++ -:keywords: {ml-init}, {stack}, {dfanalytics}, overview -:description: An introduction to {ml} {dfanalytics}, which enables you to \ -analyze your data using classification, regression, and outlier detection \ -algorithms and to generate trained models for predictions on new data. + +:frontmatter-description: An introduction to {ml} {dfanalytics}, which enables you to analyze your data using classification, regression, and outlier detection algorithms and to generate trained models for predictions on new data. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [overview] +:frontmatter-tags-user-goals: [analyze] {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-regression.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-regression.asciidoc index 0bad8e907..8ed5f14fb 100644 --- a/docs/en/stack/ml/df-analytics/ml-dfa-regression.asciidoc +++ b/docs/en/stack/ml/df-analytics/ml-dfa-regression.asciidoc @@ -3,10 +3,10 @@ [[ml-dfa-regression]] = Predicting numerical values with {regression} -:keywords: {ml-init}, {stack}, {dfanalytics}, {regression} -:description: An introduction to {ml} {regression}, which enables you to \ -predict numerical values in a data set. - +:frontmatter-description: An introduction to {ml} {regression}, which enables you to predict numerical values in a data set. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [how-to] +:frontmatter-tags-user-goals: [analyze] {reganalysis-cap} is a supervised {ml} process for estimating the relationships among different fields in your data, then making further predictions on diff --git a/docs/en/stack/ml/df-analytics/ml-how-dfa-works.asciidoc b/docs/en/stack/ml/df-analytics/ml-how-dfa-works.asciidoc index 5f2683c3e..c256b294e 100644 --- a/docs/en/stack/ml/df-analytics/ml-how-dfa-works.asciidoc +++ b/docs/en/stack/ml/df-analytics/ml-how-dfa-works.asciidoc @@ -5,10 +5,11 @@ ++++ How {dfanalytics-jobs} work ++++ -:keywords: {ml-init}, {stack}, {dfanalytics}, advanced, -:description: An explanation of how the {dfanalytics-jobs} work. Every job has \ - four or five main phases depending on its analysis type. +:frontmatter-description: An explanation of how the {dfanalytics-jobs} work. Every job has four or five main phases depending on its analysis type. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [overview] +:frontmatter-tags-user-goals: [analyze] 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/get-started/ml-getting-started.asciidoc b/docs/en/stack/ml/get-started/ml-getting-started.asciidoc index 077504d27..1bf6b5975 100644 --- a/docs/en/stack/ml/get-started/ml-getting-started.asciidoc +++ b/docs/en/stack/ml/get-started/ml-getting-started.asciidoc @@ -3,9 +3,11 @@ ++++ Tutorial: Getting started with {anomaly-detect} ++++ -:keywords: {ml-init}, {stack}, {anomaly-detect}, tutorial -:description: This tutorial shows you how to create {anomaly-jobs}, \ -interpret the results, and forecast future behavior in {kib}. + +:frontmatter-description: This tutorial shows you how to create {anomaly-jobs}, interpret the results, and forecast future behavior in {kib}. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [tutorial] +:frontmatter-tags-user-goals: [get-started] Ready to take {anomaly-detect} for a test drive? Follow this tutorial to: diff --git a/docs/en/stack/ml/machine-learning-intro.asciidoc b/docs/en/stack/ml/machine-learning-intro.asciidoc index cab88ff43..e8617a1fb 100644 --- a/docs/en/stack/ml/machine-learning-intro.asciidoc +++ b/docs/en/stack/ml/machine-learning-intro.asciidoc @@ -1,8 +1,11 @@ [chapter,role="xpack"] [[machine-learning-intro]] = What is Elastic {ml-app}? -:keywords: {ml-init}, {stack} -:description: An introduction to the breadth of Elastic {ml-features}. + +:frontmatter-description: An introduction to the breadth of Elastic {ml-features} +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [overview] +:frontmatter-tags-user-goals: [analyze] {ml-cap} features analyze your data and generate models for its patterns of behavior. The type of analysis that you choose depends on the questions or diff --git a/docs/en/stack/ml/nlp/ml-nlp-classify-text.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-classify-text.asciidoc index 7fe7048f3..e28a17131 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-classify-text.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-classify-text.asciidoc @@ -1,11 +1,10 @@ [[ml-nlp-classify-text]] = Classify text -:keywords: {ml-init}, {stack}, {nlp}, {lang-ident}, text classification, \ -zero-shot text classification - -:description: NLP tasks that classify input text or determine \ -the language of text. +:frontmatter-description: NLP tasks that classify input text or determine the language of text. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [overview] +:frontmatter-tags-user-goals: [analyze] These NLP tasks enable you to identify the language of text and classify or label unstructured input text: diff --git a/docs/en/stack/ml/nlp/ml-nlp-deploy-models.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-deploy-models.asciidoc index 589b34d42..7c1d0294b 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-deploy-models.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-deploy-models.asciidoc @@ -1,9 +1,10 @@ [[ml-nlp-deploy-models]] = Deploy trained models -:keywords: {ml-init}, {stack}, {nlp} -:description: You can import trained models into your cluster and configure them \ -for specific NLP tasks. +:frontmatter-description: You can import trained models into your cluster and configure them for specific NLP tasks. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [how-to] +:frontmatter-tags-user-goals: [analyze] If you want to perform {nlp} tasks in your cluster, you must deploy an appropriate trained model. There is tooling support in diff --git a/docs/en/stack/ml/nlp/ml-nlp-extract-info.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-extract-info.asciidoc index d5a0423df..f357175d6 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-extract-info.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-extract-info.asciidoc @@ -1,8 +1,10 @@ [[ml-nlp-extract-info]] = Extract information -:keywords: {ml-init}, {stack}, {nlp}, named entity recognition, fill mask -:description: NLP tasks that extract information from unstructured text. +:frontmatter-description: NLP tasks that extract information from unstructured text. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [overview] +:frontmatter-tags-user-goals: [analyze] These NLP tasks enable you to extract information from your unstructured text: diff --git a/docs/en/stack/ml/nlp/ml-nlp-model-ref.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-model-ref.asciidoc index 82b16a581..6ff8cf9d2 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-model-ref.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-model-ref.asciidoc @@ -1,6 +1,11 @@ [[ml-nlp-model-ref]] = Third party NLP models +:frontmatter-description: The list of compatible third party NLP models. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [reference] +:frontmatter-tags-user-goals: [analyze] + The {stack-ml-features} support transformer models that conform to the standard BERT model interface and use the WordPiece tokenization algorithm. diff --git a/docs/en/stack/ml/nlp/ml-nlp.asciidoc b/docs/en/stack/ml/nlp/ml-nlp.asciidoc index 6b98e8c94..01bf37c03 100644 --- a/docs/en/stack/ml/nlp/ml-nlp.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp.asciidoc @@ -1,8 +1,10 @@ [[ml-nlp]] = {nlp-cap} -:keywords: {ml-init}, {stack}, {nlp}, overview -:description: An introduction to {ml} {nlp} features. +:frontmatter-description: An introduction to {ml} {nlp} features. +:frontmatter-tags-products: [ml] +:frontmatter-tags-content-type: [overview] +:frontmatter-tags-user-goals: [analyze] [partintro] --