diff --git a/docs/en/ml/analyzing.asciidoc b/docs/en/ml/analyzing.asciidoc new file mode 100644 index 00000000000..d8b6640f2c8 --- /dev/null +++ b/docs/en/ml/analyzing.asciidoc @@ -0,0 +1,29 @@ +[float] +[[ml-analyzing]] +=== Analyzing the Past and Present + +The {xpackml} features automate the analysis of time-series data by creating +accurate baselines of normal behavior in the data and identifying anomalous +patterns in that data. You can submit your data for analysis in batches or +continuously in real-time {dfeeds}. + +Using proprietary {ml} algorithms, the following circumstances are detected, +scored, and linked with statistically significant influencers in the data: + +* Anomalies related to temporal deviations in values, counts, or frequencies +* Statistical rarity +* Unusual behaviors for a member of a population + +Automated periodicity detection and quick adaptation to changing data ensure +that you don’t need to specify algorithms, models, or other data science-related +configurations in order to get the benefits of {ml}. + +You can view the {ml} results in {kib} where, for example, charts illustrate the +actual data values, the bounds for the expected values, and the anomalies that +occur outside these bounds. + +[role="screenshot"] +image::images/ml-gs-job-analysis.jpg["Example screenshot from the Machine Learning Single Metric Viewer in Kibana"] + +For a more detailed walk-through of {xpackml} features, see +<>. diff --git a/docs/en/ml/images/ml-gs-job-analysis.jpg b/docs/en/ml/images/ml-gs-job-analysis.jpg new file mode 100644 index 00000000000..7f80ff9726a Binary files /dev/null and b/docs/en/ml/images/ml-gs-job-analysis.jpg differ diff --git a/docs/en/ml/index.asciidoc b/docs/en/ml/index.asciidoc index 3f6c4f1ba7f..c36f77ca812 100644 --- a/docs/en/ml/index.asciidoc +++ b/docs/en/ml/index.asciidoc @@ -3,28 +3,18 @@ [partintro] -- -The {xpackml} features automate the analysis of time-series data by creating -accurate baselines of normal behaviors in the data and identifying anomalous -patterns in that data. - -Using proprietary {ml} algorithms, the following circumstances are detected, -scored, and linked with statistically significant influencers in the data: - -* Anomalies related to temporal deviations in values, counts, or frequencies -* Statistical rarity -* Unusual behaviors for a member of a population - -Automated periodicity detection and quick adaptation to changing data ensure -that you don’t need to specify algorithms, models, or other data science-related -configurations in order to get the benefits of {ml}. - -[float] -[[ml-intro]] -== Integration with the Elastic Stack - Machine learning is tightly integrated with the Elastic Stack. Data is pulled from {es} for analysis and anomaly results are displayed in {kib} dashboards. +* <> +* <> +* <> +* <> +* <> +* <> +* <> + + -- include::overview.asciidoc[] diff --git a/docs/en/ml/overview.asciidoc b/docs/en/ml/overview.asciidoc index 404697320c4..df396615342 100644 --- a/docs/en/ml/overview.asciidoc +++ b/docs/en/ml/overview.asciidoc @@ -1,6 +1,14 @@ -[[ml-concepts]] +[[ml-overview]] == Overview +include::analyzing.asciidoc[] + +[[ml-concepts]] +=== Basic Machine Learning Terms +++++ +Basic Terms +++++ + There are a few concepts that are core to {ml} in {xpack}. Understanding these concepts from the outset will tremendously help ease the learning process.