From ac264c85ce1f292745d6f5715bfb7209979618d2 Mon Sep 17 00:00:00 2001 From: dedemorton Date: Tue, 19 Nov 2024 15:34:49 -0800 Subject: [PATCH] Change aiops to ml --- docs/en/serverless/aiops/aiops.asciidoc | 24 ------------------- .../alerting/create-manage-rules.asciidoc | 2 +- ...iidoc => generate-anomaly-alerts.asciidoc} | 8 +++---- docs/en/serverless/index.asciidoc | 16 ++++++------- .../logging/log-monitoring.asciidoc | 2 +- .../analyze-spikes.asciidoc} | 4 ++-- .../detect-anomalies.asciidoc} | 16 ++++++------- .../detect-change-points.asciidoc} | 2 +- .../forecast-anomaly.asciidoc} | 4 ++-- .../machine-learning.asciidoc | 24 +++++++++++++++++++ .../tune-anomaly-detection-job.asciidoc} | 2 +- .../observability-overview.asciidoc | 6 ++--- .../monitor-hosts-with-elastic-agent.asciidoc | 8 +++---- .../what-is-observability-serverless.asciidoc | 2 +- 14 files changed, 60 insertions(+), 60 deletions(-) delete mode 100644 docs/en/serverless/aiops/aiops.asciidoc rename docs/en/serverless/alerting/{aiops-generate-anomaly-alerts.asciidoc => generate-anomaly-alerts.asciidoc} (96%) rename docs/en/serverless/{aiops/aiops-analyze-spikes.asciidoc => machine-learning/analyze-spikes.asciidoc} (96%) rename docs/en/serverless/{aiops/aiops-detect-anomalies.asciidoc => machine-learning/detect-anomalies.asciidoc} (97%) rename docs/en/serverless/{aiops/aiops-detect-change-points.asciidoc => machine-learning/detect-change-points.asciidoc} (98%) rename docs/en/serverless/{aiops/aiops-forecast-anomaly.asciidoc => machine-learning/forecast-anomaly.asciidoc} (92%) create mode 100644 docs/en/serverless/machine-learning/machine-learning.asciidoc rename docs/en/serverless/{aiops/aiops-tune-anomaly-detection-job.asciidoc => machine-learning/tune-anomaly-detection-job.asciidoc} (99%) diff --git a/docs/en/serverless/aiops/aiops.asciidoc b/docs/en/serverless/aiops/aiops.asciidoc deleted file mode 100644 index a211dd1a4e..0000000000 --- a/docs/en/serverless/aiops/aiops.asciidoc +++ /dev/null @@ -1,24 +0,0 @@ -[[observability-aiops]] -= AIOps - -// :description: Automate anomaly detection and accelerate root cause analysis with AIOps. -// :keywords: serverless, observability, overview - -preview:[] - -The AIOps capabilities available in {obs-serverless} enable you to consume and process large observability data sets at scale, reducing the time and effort required to detect, understand, investigate, and resolve incidents. -Built on predictive analytics and {ml}, our AIOps capabilities require no prior experience with {ml}. -DevOps engineers, SREs, and security analysts can get started right away using these AIOps features with little or no advanced configuration: - -|=== -| Feature | Description - -| <> -| Detect anomalies by comparing real-time and historical data from different sources to look for unusual, problematic patterns. - -| <> -| Find and investigate the causes of unusual spikes or drops in log rates. - -| <> -| Detect distribution changes, trend changes, and other statistically significant change points in a metric of your time series data. -|=== diff --git a/docs/en/serverless/alerting/create-manage-rules.asciidoc b/docs/en/serverless/alerting/create-manage-rules.asciidoc index 2c9bfbefde..65e185d090 100644 --- a/docs/en/serverless/alerting/create-manage-rules.asciidoc +++ b/docs/en/serverless/alerting/create-manage-rules.asciidoc @@ -24,7 +24,7 @@ Learn more about Observability rules and how to create them: |=== | Rule type | Name | Detects when... -| AIOps +| Machine learning | <> | Anomalies match specific conditions. diff --git a/docs/en/serverless/alerting/aiops-generate-anomaly-alerts.asciidoc b/docs/en/serverless/alerting/generate-anomaly-alerts.asciidoc similarity index 96% rename from docs/en/serverless/alerting/aiops-generate-anomaly-alerts.asciidoc rename to docs/en/serverless/alerting/generate-anomaly-alerts.asciidoc index 82b755e2ef..e24510f050 100644 --- a/docs/en/serverless/alerting/aiops-generate-anomaly-alerts.asciidoc +++ b/docs/en/serverless/alerting/generate-anomaly-alerts.asciidoc @@ -1,4 +1,4 @@ -[[observability-aiops-generate-anomaly-alerts]] +[[observability-generate-anomaly-alerts]] = Create an anomaly detection rule // :description: Get alerts when anomalies match specific conditions. @@ -29,7 +29,7 @@ To create an anomaly detection rule: . In your {obs-serverless} project, go to **AIOps** → **Anomaly detection**. . In the list of anomaly detection jobs, find the job you want to check for anomalies. -Haven't created a job yet? <>. +Haven't created a job yet? <>. . From the **Actions** menu next to the job, select **Create alert rule**. . Specify a name and optional tags for the rule. You can use these tags later to filter alerts. . Verify that the correct job is selected and configure the alert details: @@ -80,7 +80,7 @@ Alerts generated by these rules do not appear on the **Alerts** page. ==== [discrete] -[[observability-aiops-generate-anomaly-alerts-add-actions]] +[[observability-generate-anomaly-alerts-add-actions]] == Add actions You can extend your rules with actions that interact with third-party systems, write to logs or indices, or send user notifications. You can add an action to a rule at any time. You can create rules without adding actions, and you can also define multiple actions for a single rule. @@ -189,7 +189,7 @@ The typical value for the bucket, according to analytical modeling. ===== [discrete] -[[observability-aiops-generate-anomaly-alerts-edit-an-anomaly-detection-rule]] +[[observability-generate-anomaly-alerts-edit-an-anomaly-detection-rule]] == Edit an anomaly detection rule To edit an anomaly detection rule: diff --git a/docs/en/serverless/index.asciidoc b/docs/en/serverless/index.asciidoc index cbe44a855c..dcf68f200d 100644 --- a/docs/en/serverless/index.asciidoc +++ b/docs/en/serverless/index.asciidoc @@ -160,7 +160,7 @@ include::./incident-management.asciidoc[leveloffset=+2] // Alerting include::./alerting/alerting.asciidoc[leveloffset=+3] include::./alerting/create-manage-rules.asciidoc[leveloffset=+4] -include::./alerting/aiops-generate-anomaly-alerts.asciidoc[leveloffset=+5] +include::./alerting/generate-anomaly-alerts.asciidoc[leveloffset=+5] include::./alerting/create-anomaly-alert-rule.asciidoc[leveloffset=+5] include::./alerting/create-custom-threshold-alert-rule.asciidoc[leveloffset=+5] include::./alerting/create-elasticsearch-query-alert-rule.asciidoc[leveloffset=+5] @@ -194,14 +194,14 @@ include::./monitor-datasets.asciidoc[leveloffset=+2] //Observability AI Assistant include::./ai-assistant/ai-assistant.asciidoc[leveloffset=+2] -//AIOPS +//Machine learning -include::./aiops/aiops.asciidoc[leveloffset=+2] -include::./aiops/aiops-detect-anomalies.asciidoc[leveloffset=+3] -include::./aiops/aiops-tune-anomaly-detection-job.asciidoc[leveloffset=+4] -include::./aiops/aiops-forecast-anomaly.asciidoc[leveloffset=+4] -include::./aiops/aiops-analyze-spikes.asciidoc[leveloffset=+3] -include::./aiops/aiops-detect-change-points.asciidoc[leveloffset=+3] +include::./machine-learning/machine-learning.asciidoc[leveloffset=+2] +include::./machine-learning/machine-learning-detect-anomalies.asciidoc[leveloffset=+3] +include::./machine-learning/machine-learning-tune-anomaly-detection-job.asciidoc[leveloffset=+4] +include::./machine-learning/machine-learning-forecast-anomaly.asciidoc[leveloffset=+4] +include::./machine-learning/machine-learning-analyze-spikes.asciidoc[leveloffset=+3] +include::./machine-learning/machine-learning-detect-change-points.asciidoc[leveloffset=+3] // Reference group diff --git a/docs/en/serverless/logging/log-monitoring.asciidoc b/docs/en/serverless/logging/log-monitoring.asciidoc index 80a600b4ef..33e6d15f1d 100644 --- a/docs/en/serverless/logging/log-monitoring.asciidoc +++ b/docs/en/serverless/logging/log-monitoring.asciidoc @@ -96,7 +96,7 @@ Use **Logs Explorer** to search, filter, and tail all your logs ingested into yo The following resources provide information on viewing and monitoring your logs: * <>: Discover and explore all of the log events flowing in from your servers, virtual machines, and containers in a centralized view. -* <>: Use {ml} to detect log anomalies automatically. +* <>: Use {ml} to detect log anomalies automatically. [discrete] [[observability-log-monitoring-monitor-data-sets]] diff --git a/docs/en/serverless/aiops/aiops-analyze-spikes.asciidoc b/docs/en/serverless/machine-learning/analyze-spikes.asciidoc similarity index 96% rename from docs/en/serverless/aiops/aiops-analyze-spikes.asciidoc rename to docs/en/serverless/machine-learning/analyze-spikes.asciidoc index 3673f62b34..6fb5f344d3 100644 --- a/docs/en/serverless/aiops/aiops-analyze-spikes.asciidoc +++ b/docs/en/serverless/machine-learning/analyze-spikes.asciidoc @@ -1,4 +1,4 @@ -[[observability-aiops-analyze-spikes]] +[[observability-analyze-spikes]] = Analyze log spikes and drops // :description: Find and investigate the causes of unusual spikes or drops in log rates. @@ -57,7 +57,7 @@ It also helps you group logs in ways that go beyond what you can achieve with a To run log pattern analysis: -. Follow the steps under <> to run a log rate analysis. +. Follow the steps under <> to run a log rate analysis. . From the **Actions** menu, choose **View in Log Pattern Analysis**. . Select a category field and optionally apply any filters that you want. . Click **Run pattern analysis**. diff --git a/docs/en/serverless/aiops/aiops-detect-anomalies.asciidoc b/docs/en/serverless/machine-learning/detect-anomalies.asciidoc similarity index 97% rename from docs/en/serverless/aiops/aiops-detect-anomalies.asciidoc rename to docs/en/serverless/machine-learning/detect-anomalies.asciidoc index 37211e6686..f72484a083 100644 --- a/docs/en/serverless/aiops/aiops-detect-anomalies.asciidoc +++ b/docs/en/serverless/machine-learning/detect-anomalies.asciidoc @@ -1,4 +1,4 @@ -[[observability-aiops-detect-anomalies]] +[[observability-detect-anomalies]] = Detect anomalies // :description: Detect anomalies by comparing real-time and historical data from different sources to look for unusual, problematic patterns. @@ -102,7 +102,7 @@ Expand the fields to see details about the range and distribution of values. When you're done, go back to the first step and create your job. ==== . Step through the instructions in the job creation wizard to configure your job. -You can accept the default settings for most settings now and <> later. +You can accept the default settings for most settings now and <> later. . If you want the job to start immediately when the job is created, make sure that option is selected on the summary page. . When you're done, click **Create job**. When the job runs, the {ml} features analyze the input stream of data, model its behavior, and perform analysis based on the detectors in each job. @@ -110,7 +110,7 @@ When an event occurs outside of the baselines of normal behavior, that event is . After the job is started, click **View results**. [discrete] -[[observability-aiops-detect-anomalies-view-the-results]] +[[observability-detect-anomalies-view-the-results]] == View the results After the anomaly detection job has processed some data, @@ -188,7 +188,7 @@ By default, the **Anomalies** table contains all anomalies that have a severity If you are only interested in critical anomalies, for example, you can change the severity threshold for this table. . (Optional) From the **Actions** menu in the **Anomalies** table, you can choose to view relevant documents in **Discover** or create a job rule. Job rules instruct anomaly detectors to change their behavior based on domain-specific knowledge that you provide. -To learn more, refer to <> +To learn more, refer to <> After you have identified anomalies, often the next step is to try to determine the context of those situations. For example, are there other factors that are @@ -265,11 +265,11 @@ The list of anomalies uses the record-level anomaly scores. ==== [discrete] -[[observability-aiops-detect-anomalies-next-steps]] +[[observability-detect-anomalies-next-steps]] == Next steps After setting up an anomaly detection job, you may want to: -* <> -* <> -* <> +* <> +* <> +* <> diff --git a/docs/en/serverless/aiops/aiops-detect-change-points.asciidoc b/docs/en/serverless/machine-learning/detect-change-points.asciidoc similarity index 98% rename from docs/en/serverless/aiops/aiops-detect-change-points.asciidoc rename to docs/en/serverless/machine-learning/detect-change-points.asciidoc index 67d1547931..8983bf4818 100644 --- a/docs/en/serverless/aiops/aiops-detect-change-points.asciidoc +++ b/docs/en/serverless/machine-learning/detect-change-points.asciidoc @@ -1,4 +1,4 @@ -[[observability-aiops-detect-change-points]] +[[observability-detect-change-points]] = Detect change points // :description: Detect distribution changes, trend changes, and other statistically significant change points in a metric of your time series data. diff --git a/docs/en/serverless/aiops/aiops-forecast-anomaly.asciidoc b/docs/en/serverless/machine-learning/forecast-anomaly.asciidoc similarity index 92% rename from docs/en/serverless/aiops/aiops-forecast-anomaly.asciidoc rename to docs/en/serverless/machine-learning/forecast-anomaly.asciidoc index 274d1979b0..661e6c8c02 100644 --- a/docs/en/serverless/aiops/aiops-forecast-anomaly.asciidoc +++ b/docs/en/serverless/machine-learning/forecast-anomaly.asciidoc @@ -1,4 +1,4 @@ -[[observability-aiops-forecast-anomalies]] +[[observability-forecast-anomalies]] = Forecast future behavior // :description: Predict future behavior of your data by creating a forecast for an anomaly detection job. @@ -25,7 +25,7 @@ For example, you might want to determine how likely it is that your disk utiliza To create a forecast: -. <> and view the results in the **Single Metric Viewer**. +. <> and view the results in the **Single Metric Viewer**. . Click **Forecast**. . Specify a duration for your forecast. This value indicates how far to extrapolate beyond the last record that was processed. diff --git a/docs/en/serverless/machine-learning/machine-learning.asciidoc b/docs/en/serverless/machine-learning/machine-learning.asciidoc new file mode 100644 index 0000000000..a328e259ff --- /dev/null +++ b/docs/en/serverless/machine-learning/machine-learning.asciidoc @@ -0,0 +1,24 @@ +[[observability-machine-learning]] += Machine learning + +// :description: Automate anomaly detection and accelerate root cause analysis with machine learning. +// :keywords: serverless, observability, overview + +preview:[] + +The machine learning capabilities available in {obs-serverless} enable you to consume and process large observability data sets at scale, reducing the time and effort required to detect, understand, investigate, and resolve incidents. +Built on predictive analytics and {ml}, these capabilities require no prior experience with {ml}. +DevOps engineers, SREs, and security analysts can get started right away using these features with little or no advanced configuration: + +|=== +| Feature | Description + +| <> +| Detect anomalies by comparing real-time and historical data from different sources to look for unusual, problematic patterns. + +| <> +| Find and investigate the causes of unusual spikes or drops in log rates. + +| <> +| Detect distribution changes, trend changes, and other statistically significant change points in a metric of your time series data. +|=== diff --git a/docs/en/serverless/aiops/aiops-tune-anomaly-detection-job.asciidoc b/docs/en/serverless/machine-learning/tune-anomaly-detection-job.asciidoc similarity index 99% rename from docs/en/serverless/aiops/aiops-tune-anomaly-detection-job.asciidoc rename to docs/en/serverless/machine-learning/tune-anomaly-detection-job.asciidoc index d21bf82521..11f7bf0e48 100644 --- a/docs/en/serverless/aiops/aiops-tune-anomaly-detection-job.asciidoc +++ b/docs/en/serverless/machine-learning/tune-anomaly-detection-job.asciidoc @@ -1,4 +1,4 @@ -[[observability-aiops-tune-anomaly-detection-job]] +[[observability-tune-anomaly-detection-job]] = Tune your anomaly detection job // :description: Tune your job by creating calendars, adding job rules, and defining custom URLs. diff --git a/docs/en/serverless/observability-overview.asciidoc b/docs/en/serverless/observability-overview.asciidoc index f27d9b70c3..4a8da085ab 100644 --- a/docs/en/serverless/observability-overview.asciidoc +++ b/docs/en/serverless/observability-overview.asciidoc @@ -135,8 +135,8 @@ image::images/cases.png[Screenshot showing list of cases] <> [discrete] -[[observability-serverless-observability-overview-aiops]] -== AIOps +[[observability-serverless-observability-overview-machine-learning]] +== Machine learning Reduce the time and effort required to detect, understand, investigate, and resolve incidents at scale by leveraging predictive analytics and machine learning: @@ -148,4 +148,4 @@ by leveraging predictive analytics and machine learning: [role="screenshot"] image::images/log-rate-analysis.png[Log rate analysis page showing log rate spike ] -<> +<> diff --git a/docs/en/serverless/quickstarts/monitor-hosts-with-elastic-agent.asciidoc b/docs/en/serverless/quickstarts/monitor-hosts-with-elastic-agent.asciidoc index 25d709cd67..bdb9883e2a 100644 --- a/docs/en/serverless/quickstarts/monitor-hosts-with-elastic-agent.asciidoc +++ b/docs/en/serverless/quickstarts/monitor-hosts-with-elastic-agent.asciidoc @@ -119,10 +119,10 @@ You can also: ** <> to find degraded documents. ** <> to find patterns in unstructured log messages. ** <> that notify you when an Observability data type reaches or exceeds a given value. -* Use <> to apply predictive analytics and machine learning to your data: +* Use <> to apply predictive analytics and machine learning to your data: + -** <> by comparing real-time and historical data from different sources to look for unusual, problematic patterns. -** <>. -** <> in your time series data. +** <> by comparing real-time and historical data from different sources to look for unusual, problematic patterns. +** <>. +** <> in your time series data. Refer to <> for a description of other useful features. diff --git a/docs/en/serverless/what-is-observability-serverless.asciidoc b/docs/en/serverless/what-is-observability-serverless.asciidoc index 6184f3d518..76d6ee1f4f 100644 --- a/docs/en/serverless/what-is-observability-serverless.asciidoc +++ b/docs/en/serverless/what-is-observability-serverless.asciidoc @@ -26,7 +26,7 @@ While in technical preview, Elastic Observability serverless projects should not * <>: Use Discover to explore your log data. * <>: Create rules to detect complex conditions and trigger alerts. * <>: Measure key metrics important to the business. -* <>: Find unusual behavior in time series data. +* <>: Find unusual behavior in time series data. * <>: Monitor your software services and applications in real time. * <>: Reuse existing APM instrumentation to capture logs, traces, and metrics. * <>: Get a metrics-driven view of your hosts backed by an interface called Lens.