From a1bab88b8477b2b7e13f7ebf5e8b4d15c1646201 Mon Sep 17 00:00:00 2001 From: kosabogi Date: Thu, 31 Oct 2024 13:09:28 +0100 Subject: [PATCH 1/3] Updates phrasing when referring to pages --- .../anomaly-detection/ml-ad-run-jobs.asciidoc | 5 +- .../ml-detect-categories.asciidoc | 2 +- .../ml-jobs-from-visuals.asciidoc | 2 +- .../ml-population-analysis.asciidoc | 2 +- .../ml-revert-model-snapshot.asciidoc | 2 +- .../ml/df-analytics/ml-dfa-shared.asciidoc | 2 +- .../ml/get-started/ml-gs-results.asciidoc | 4 +- .../ml/get-started/ml-gs-visualizer.asciidoc | 2 +- docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc | 4 +- docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc | 6 +- .../stack/ml/nlp/ml-nlp-ner-example.asciidoc | 2 +- docs/en/stack/ml/setup.asciidoc | 79 +++++++++---------- 12 files changed, 56 insertions(+), 56 deletions(-) diff --git a/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc index 56e3a7473..93dd611cb 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc @@ -33,8 +33,9 @@ a {dfeed} will be required. You can create {anomaly-jobs} by using the {ref}/ml-put-job.html[create {anomaly-jobs} API]. {kib} also provides wizards to simplify the process, which vary depending on whether you are using -the {ml-app} app, {security-app} or {observability} apps. In *{ml-app}* > -*Anomaly Detection*: +the {ml-app} app, {security-app} or {observability} apps. To open *Anomaly Detection*, +find *{ml-app}* in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +In *{ml-app}* > *Anomaly Detection*: [role="screenshot"] image::images/ml-create-job.png[Create New Job] diff --git a/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc index fbd0aefe3..2f8471cc6 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc @@ -33,7 +33,7 @@ Avoid using human-generated data for categorization analysis. [[creating-categorization-jobs]] == Creating categorization jobs -. In {kib}, navigate to **{ml-app} > Anomaly Detection > Jobs**. +. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. . Click **Create job**, select the {data-view} you want to analyze. . Select the **Categorization** wizard from the list. . Choose a categorization detector - it's the `count` function in this example - and the field you want to categorize - the `message` field in this example. diff --git a/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc index 18b164cf9..e580fd2db 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc @@ -40,7 +40,7 @@ NOTE: You need to have a compatible visualization on **Dashboard** to create an which is based on the {kib} sample flight data set. Select the `Flight count` visualization from the dashboard. -. Go to **Analytics > Dashboard** and select a dashboard with a compatible +. Go to **Analytics > Dashboard** from the main menu, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. Select a dashboard with a compatible visualization. . Open the **Options (...) menu** for the panel, then select **More**. . Select **Create {anomaly-job}**. The option is only displayed if the diff --git a/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc index 6156f0728..4356fb939 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc @@ -27,7 +27,7 @@ Population analysis is resource-efficient and scales well, enabling the analysis [[creating-population-jobs]] == Creating population jobs -. In {kib}, navigate to **{ml-app} > Anomaly Detection > Jobs**. +. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. . Click **Create job**, select the {data-source} you want to analyze. . Select the **Population** wizard from the list. . Choose a population field - it's the `clientip` field in this example - and the metric you want to use for the analysis - `Mean(bytes)` in this example. diff --git a/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc index 08f494edc..df94e9d48 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc @@ -7,7 +7,7 @@ resilience. It makes it possible to reset the model to a previous state in case of a system failure or if the model changed significantly due to a one-off event. -. In {kib}, navigate to **{ml-app} > Anomaly Detection > Jobs**. +. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. . Locate the {anomaly-job} whose model you want to revert in the job table. . Open the job details and navigate to the **Model Snapshots** tab. + diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc index 06486283d..1ba3aed3e 100644 --- a/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc +++ b/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc @@ -1,6 +1,6 @@ tag::dfa-deploy-model[] . To deploy {dfanalytics} model in a pipeline, navigate to **Machine Learning** > -**Model Management** > **Trained models** in {kib}. +**Model Management** > **Trained models**, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. . Find the model you want to deploy in the list and click **Deploy model** in the **Actions** menu. diff --git a/docs/en/stack/ml/get-started/ml-gs-results.asciidoc b/docs/en/stack/ml/get-started/ml-gs-results.asciidoc index 489053679..f5697d261 100644 --- a/docs/en/stack/ml/get-started/ml-gs-results.asciidoc +++ b/docs/en/stack/ml/get-started/ml-gs-results.asciidoc @@ -34,7 +34,7 @@ request rate on your web site drops significantly. Let's start by looking at this simple job in the **Single Metric Viewer**: -. Select the *Anomaly Detection* tab in *{ml-app}* to see the list of your +. Select the *Jobs* tab in *{ml-app}* to see the list of your {anomaly-jobs}. . Click the chart icon in the *Actions* column for your `low_request_rate` job @@ -151,7 +151,7 @@ look at both high and low request rates partitioned by response code. Let's start by looking at the `response_code_rates` job in the **Anomaly Explorer**: -. Select the *Anomaly Detection* tab in *{ml-app}* to see the list of your +. Select the *Jobs* tab in *{ml-app}* to see the list of your {anomaly-jobs}. . Open the `response_code_rates` job in the Anomaly Explorer to view its results diff --git a/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc b/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc index a1dcc9da6..d210f1208 100644 --- a/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc +++ b/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc @@ -17,7 +17,7 @@ exception for your {kib} URL. -- -. Click *Machine Learning* in the {kib} main menu. +. Open the *Machine Learning* page in {kib}. Find *Machine Learning* in the main menu, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. . Select the *{data-viz}* tab. diff --git a/docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc index e14a925c9..9b7e463cd 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc @@ -92,7 +92,7 @@ NOTE: For most cases, the preferred version is the **Intel and Linux optimized** [[trained-model-e5]] ==== Using the Trained Models page -1. In {kib}, navigate to **{ml-app}** > **Trained Models**. E5 can be found in +1. In {kib}, navigate to **{ml-app}** > **Trained Models** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. E5 can be found in the list of trained models. There are two versions available: one portable version which runs on any hardware and one version which is optimized for Intel® silicon. You can see which model is recommended to use based on your hardware @@ -250,7 +250,7 @@ xpack.ml.model_repository: file://${path.home}/config/models/` . Repeat step 2 and step 3 on all master-eligible nodes. . {ref}/restart-cluster.html#restart-cluster-rolling[Restart] the master-eligible nodes one by one. -. Navigate to the **Trained Models** page in {kib}, E5 can be found in the +. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. E5 can be found in the list of trained models. . Click the **Add trained model** button, select the E5 model version you downloaded in step 1 and want to deploy and click **Download**. The selected diff --git a/docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc index 7a2d6acd4..92bbefd01 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc @@ -350,7 +350,7 @@ master-eligible nodes can reach the server you specify. . Repeat step 5 on all master-eligible nodes. . {ref}/restart-cluster.html#restart-cluster-rolling[Restart] the master-eligible nodes one by one. -. Navigate to the **Trained Models** page in {kib}, ELSER can be found in the +. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. ELSER can be found in the list of trained models. . Click the **Add trained model** button, select the ELSER model version you downloaded in step 1 and want to deploy, and click **Download**. The selected @@ -390,7 +390,7 @@ xpack.ml.model_repository: file://${path.home}/config/models/` . Repeat step 2 and step 3 on all master-eligible nodes. . {ref}/restart-cluster.html#restart-cluster-rolling[Restart] the master-eligible nodes one by one. -. Navigate to the **Trained Models** page in {kib}, ELSER can be found in the +. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. ELSER can be found in the list of trained models. . Click the **Add trained model** button, select the ELSER model version you downloaded in step 1 and want to deploy and click **Download**. The selected @@ -406,7 +406,7 @@ allocations and threads per allocation values. == Testing ELSER You can test the deployed model in {kib}. Navigate to **Model Management** > -**Trained Models**, locate the deployed ELSER model in the list of trained +**Trained Models** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. Locate the deployed ELSER model in the list of trained models, then select **Test model** from the Actions menu. You can use data from an existing index to test the model. Select the index, diff --git a/docs/en/stack/ml/nlp/ml-nlp-ner-example.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-ner-example.asciidoc index 7d25a380c..18dd8dc48 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-ner-example.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-ner-example.asciidoc @@ -294,7 +294,7 @@ You can create a tag cloud to visualize your data processed by the {infer} pipeline. A tag cloud is a visualization that scales words by the frequency at which they occur. It is a handy tool for viewing the entities found in the data. -In {kib}, open **Stack management** > **{data-sources-cap}**, and create a new +In {kib}, open **Stack management** > **{data-sources-cap}** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar], and create a new {data-source} from the `les-miserables-infer` index pattern. Open **Dashboard** and create a new dashboard. Select the diff --git a/docs/en/stack/ml/setup.asciidoc b/docs/en/stack/ml/setup.asciidoc index 27fd55af8..9558fef27 100644 --- a/docs/en/stack/ml/setup.asciidoc +++ b/docs/en/stack/ml/setup.asciidoc @@ -11,17 +11,16 @@ To use the {stack} {ml-features}, you must have: -[%interactive] -- [ ] the {subscriptions}[appropriate subscription] level or the free trial +- the {subscriptions}[appropriate subscription] level or the free trial period activated -- [ ] `xpack.ml.enabled` set to its default value of `true` on every node in the +- `xpack.ml.enabled` set to its default value of `true` on every node in the cluster (refer to {ref}/ml-settings.html[{ml-cap} settings in {es}]) -- [ ] `ml` value defined in the list of `node.roles` on the +- `ml` value defined in the list of `node.roles` on the {ref}/modules-node.html#ml-node[{ml} nodes] -- [ ] {ml} features visible in the {kib} space -- [ ] security privileges assigned to the user that: - * grant use of {ml-features}, and - * grant access to source and destination indices. +- {ml} features visible in the {kib} space +- security privileges assigned to the user that: + * grant use of {ml-features}, and + * grant access to source and destination indices. TIP: The fastest way to get started with {ml-features} is to {ess-trial}[start a free 14-day trial of {ess}] in the cloud. @@ -39,12 +38,15 @@ the two main categories: * *<>*: uses the {ml-features} in {kib} and does not use Dev Tools. It requires either {kib} feature privileges or {es} security privileges and is granted the most permissive combination of both. {kib} feature -privileges are recommended if you control job level visibility via _Spaces_. +privileges are recommended if you control job level visibility via **Spaces**. {ml-cap} features must be visible in the relevant space. Refer to <> for configuration information. -You can configure these privileges under **{stack-manage-app}** > _Security_ in -{kib} or via the respective {es} security APIs. +You can configure these privileges + +- under **Security**. To open Security, find **{stack-manage-app}** in the main menu or +use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +- via the respective {es} security APIs. [discrete] @@ -55,19 +57,17 @@ If you use {ml} APIs, you must have the following cluster and index privileges: For full access: -[%interactive] -* [ ] `machine_learning_admin` built-in role or the equivalent cluster +* `machine_learning_admin` built-in role or the equivalent cluster privileges -* [ ] `read` and `view_index_metadata` on source indices -* [ ] `read`, `manage`, and `index` on destination indices (for +* `read` and `view_index_metadata` on source indices +* `read`, `manage`, and `index` on destination indices (for {dfanalytics-jobs} only) For read-only access: -[%interactive] -* [ ] `machine_learning_user` built-in role or the equivalent cluster privileges -* [ ] `read` index privileges on source indices -* [ ] `read` index privileges on destination indices (for {dfanalytics-jobs} +* `machine_learning_user` built-in role or the equivalent cluster privileges +* `read` index privileges on source indices +* `read` index privileges on destination indices (for {dfanalytics-jobs} only) IMPORTANT: The `machine_learning_admin` and `machine_learning_user` built-in @@ -92,19 +92,21 @@ visualizations as well as {ml} job, trained model and module saved objects. In {kib}, the {ml-features} must be visible in your {kibana-ref}/xpack-spaces.html#spaces-control-feature-visibility[space]. To -control which features are visible in your space, use **{stack-manage-app}** > -_{kib}_ > _Spaces_. +manage which features are visible in your space, go to **{stack-manage-app}** > +**{kib}** > **Spaces** or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] +to locate **Spaces** directly. [role="screenshot"] image::spaces.jpg["Manage spaces in {kib}"] In addition to index privileges, source {data-sources} must also exist in the -same space as your {ml} jobs. These can be configured in **{stack-manage-app}** -> _{kib}_ > _{data-sources-caps}_. +same space as your {ml} jobs. You can configure these under **{data-sources-caps}**. To open **{data-sources-caps}**, +find **{stack-manage-app}** > **{kib}** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. Each {ml} job and trained model can be assigned to all, one, or multiple spaces. -This can be configured in **{stack-manage-app} > Alerts and Insights > Machine Learning**. +This can be configured in **Machine Learning**. To open **Machine Learning**, find **{stack-manage-app} > Alerts and Insights**, +or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. You can edit the spaces that a job or model is assigned to by clicking the icons in the **Spaces** column. @@ -118,22 +120,20 @@ image::assign-job-spaces.jpg["Assign machine learning jobs to spaces"] Within a {kib} space, for full access to the {ml-features}, you must have: -[%interactive] -* [ ] `Machine Learning: All` {kib} privileges -* [ ] `Data Views Management: All` {kib} feature privileges -* [ ] `read`, and `view_index_metadata` index privileges on your source indices -* [ ] {data-sources} for your source indices -* [ ] {data-sources}, `read`, `manage`, and `index` index privileges on +* `Machine Learning: All` {kib} privileges +* `Data Views Management: All` {kib} feature privileges +* `read`, and `view_index_metadata` index privileges on your source indices +* {data-sources} for your source indices +* {data-sources}, `read`, `manage`, and `index` index privileges on destination indices (for {dfanalytics-jobs} only) Within a {kib} space, for read-only access to the {ml-features}, you must have: -[%interactive] -* [ ] `Machine Learning: Read` {kib} privileges -* [ ] {data-sources} for your source indices -* [ ] `read` index privilege on your source indices -* [ ] {data-sources} and `read` index privileges on destination indices (for +* `Machine Learning: Read` {kib} privileges +* {data-sources} for your source indices +* `read` index privilege on your source indices +* {data-sources} and `read` index privileges on destination indices (for {dfanalytics-jobs} only) IMPORTANT: A user who has full or read-only access to {ml-features} within @@ -158,12 +158,11 @@ privileges and grant access to `machine_learning_admin` or Within a {kib} space, to upload and import files in the *{data-viz}*, you must have: -[%interactive] -- [ ] `Machine Learning: Read` or `Discover: All` {kib} feature privileges -- [ ] `Data Views Management: All` {kib} feature privileges -- [ ] `ingest_admin` built-in role, or `manage_ingest_pipelines` cluster +- `Machine Learning: Read` or `Discover: All` {kib} feature privileges +- `Data Views Management: All` {kib} feature privileges +- `ingest_admin` built-in role, or `manage_ingest_pipelines` cluster privilege -- [ ] `create`, `create_index`, `manage` and `read` index privileges for +- `create`, `create_index`, `manage` and `read` index privileges for destination indices For more information, see {ref}/security-privileges.html[Security privileges] From 65d47da74bf2c8e2f3ef277b9f2679b52985ef73 Mon Sep 17 00:00:00 2001 From: kosabogi Date: Mon, 4 Nov 2024 07:20:39 +0100 Subject: [PATCH 2/3] Fixes typos --- docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc | 1 - .../en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc | 2 +- .../en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc | 2 +- .../stack/ml/anomaly-detection/ml-population-analysis.asciidoc | 2 +- .../ml/anomaly-detection/ml-revert-model-snapshot.asciidoc | 2 +- docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc | 2 +- docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc | 2 +- docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc | 2 +- docs/en/stack/ml/setup.asciidoc | 2 +- 9 files changed, 8 insertions(+), 9 deletions(-) diff --git a/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc index 93dd611cb..6f2bba852 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc @@ -35,7 +35,6 @@ You can create {anomaly-jobs} by using the wizards to simplify the process, which vary depending on whether you are using the {ml-app} app, {security-app} or {observability} apps. To open *Anomaly Detection*, find *{ml-app}* in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. -In *{ml-app}* > *Anomaly Detection*: [role="screenshot"] image::images/ml-create-job.png[Create New Job] diff --git a/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc index 2f8471cc6..b9e0d20a3 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc @@ -33,7 +33,7 @@ Avoid using human-generated data for categorization analysis. [[creating-categorization-jobs]] == Creating categorization jobs -. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. . Click **Create job**, select the {data-view} you want to analyze. . Select the **Categorization** wizard from the list. . Choose a categorization detector - it's the `count` function in this example - and the field you want to categorize - the `message` field in this example. diff --git a/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc index e580fd2db..27be3da5a 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc @@ -40,7 +40,7 @@ NOTE: You need to have a compatible visualization on **Dashboard** to create an which is based on the {kib} sample flight data set. Select the `Flight count` visualization from the dashboard. -. Go to **Analytics > Dashboard** from the main menu, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. Select a dashboard with a compatible +. Go to **Analytics > Dashboard** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. Select a dashboard with a compatible visualization. . Open the **Options (...) menu** for the panel, then select **More**. . Select **Create {anomaly-job}**. The option is only displayed if the diff --git a/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc index 4356fb939..8dc2e0b54 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc @@ -27,7 +27,7 @@ Population analysis is resource-efficient and scales well, enabling the analysis [[creating-population-jobs]] == Creating population jobs -. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. . Click **Create job**, select the {data-source} you want to analyze. . Select the **Population** wizard from the list. . Choose a population field - it's the `clientip` field in this example - and the metric you want to use for the analysis - `Mean(bytes)` in this example. diff --git a/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc index df94e9d48..9426a49fe 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc @@ -7,7 +7,7 @@ resilience. It makes it possible to reset the model to a previous state in case of a system failure or if the model changed significantly due to a one-off event. -. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. . Locate the {anomaly-job} whose model you want to revert in the job table. . Open the job details and navigate to the **Model Snapshots** tab. + diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc index 1ba3aed3e..68e152f65 100644 --- a/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc +++ b/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc @@ -1,6 +1,6 @@ tag::dfa-deploy-model[] . To deploy {dfanalytics} model in a pipeline, navigate to **Machine Learning** > -**Model Management** > **Trained models**, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. +**Model Management** > **Trained models** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. . Find the model you want to deploy in the list and click **Deploy model** in the **Actions** menu. diff --git a/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc b/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc index d210f1208..d9b77e77f 100644 --- a/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc +++ b/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc @@ -17,7 +17,7 @@ exception for your {kib} URL. -- -. Open the *Machine Learning* page in {kib}. Find *Machine Learning* in the main menu, or or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +. Open *Machine Learning* from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. . Select the *{data-viz}* tab. diff --git a/docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc index 09194c2c7..ef1f8e37d 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc @@ -18,7 +18,7 @@ can use it to perform {nlp} tasks in ingest pipelines. == Add an {infer} processor to an ingest pipeline In {kib}, you can create and edit pipelines in **{stack-manage-app}** > -**Ingest Pipelines**. +**Ingest Pipelines**. To open **Ingest Pipelines**, find **{stack-manage-app}** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. [role="screenshot"] image::images/ml-nlp-pipeline-lang.png[Creating a pipeline in the Stack Management app,align="center"] diff --git a/docs/en/stack/ml/setup.asciidoc b/docs/en/stack/ml/setup.asciidoc index 9558fef27..c8854e3e4 100644 --- a/docs/en/stack/ml/setup.asciidoc +++ b/docs/en/stack/ml/setup.asciidoc @@ -105,7 +105,7 @@ find **{stack-manage-app}** > **{kib}** in the main menu, or use the {kibana-ref Each {ml} job and trained model can be assigned to all, one, or multiple spaces. -This can be configured in **Machine Learning**. To open **Machine Learning**, find **{stack-manage-app} > Alerts and Insights**, +This can be configured in **Machine Learning**. To open **Machine Learning**, find **{stack-manage-app} > Alerts and Insights** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. You can edit the spaces that a job or model is assigned to by clicking the icons in the **Spaces** column. From 61d00feed3b2898af1461857cdf2c3e7b04f67a6 Mon Sep 17 00:00:00 2001 From: kosabogi Date: Mon, 4 Nov 2024 12:00:04 +0100 Subject: [PATCH 3/3] Replaces 'global search bar' with 'global search field' --- .../en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc | 2 +- .../ml/anomaly-detection/ml-detect-categories.asciidoc | 2 +- .../ml/anomaly-detection/ml-jobs-from-visuals.asciidoc | 2 +- .../ml/anomaly-detection/ml-population-analysis.asciidoc | 2 +- .../anomaly-detection/ml-revert-model-snapshot.asciidoc | 2 +- docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc | 2 +- docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc | 2 +- docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc | 4 ++-- docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc | 6 +++--- docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc | 2 +- docs/en/stack/ml/nlp/ml-nlp-ner-example.asciidoc | 2 +- docs/en/stack/ml/setup.asciidoc | 8 ++++---- 12 files changed, 18 insertions(+), 18 deletions(-) diff --git a/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc index 6f2bba852..e56648d71 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc @@ -34,7 +34,7 @@ You can create {anomaly-jobs} by using the {ref}/ml-put-job.html[create {anomaly-jobs} API]. {kib} also provides wizards to simplify the process, which vary depending on whether you are using the {ml-app} app, {security-app} or {observability} apps. To open *Anomaly Detection*, -find *{ml-app}* in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +find *{ml-app}* in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. [role="screenshot"] image::images/ml-create-job.png[Create New Job] diff --git a/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc index b9e0d20a3..d1a70e821 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc @@ -33,7 +33,7 @@ Avoid using human-generated data for categorization analysis. [[creating-categorization-jobs]] == Creating categorization jobs -. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. . Click **Create job**, select the {data-view} you want to analyze. . Select the **Categorization** wizard from the list. . Choose a categorization detector - it's the `count` function in this example - and the field you want to categorize - the `message` field in this example. diff --git a/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc index 27be3da5a..2a3857027 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc @@ -40,7 +40,7 @@ NOTE: You need to have a compatible visualization on **Dashboard** to create an which is based on the {kib} sample flight data set. Select the `Flight count` visualization from the dashboard. -. Go to **Analytics > Dashboard** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. Select a dashboard with a compatible +. Go to **Analytics > Dashboard** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. Select a dashboard with a compatible visualization. . Open the **Options (...) menu** for the panel, then select **More**. . Select **Create {anomaly-job}**. The option is only displayed if the diff --git a/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc index 8dc2e0b54..3ccd95e75 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc @@ -27,7 +27,7 @@ Population analysis is resource-efficient and scales well, enabling the analysis [[creating-population-jobs]] == Creating population jobs -. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. . Click **Create job**, select the {data-source} you want to analyze. . Select the **Population** wizard from the list. . Choose a population field - it's the `clientip` field in this example - and the metric you want to use for the analysis - `Mean(bytes)` in this example. diff --git a/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc b/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc index 9426a49fe..478465d57 100644 --- a/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc +++ b/docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc @@ -7,7 +7,7 @@ resilience. It makes it possible to reset the model to a previous state in case of a system failure or if the model changed significantly due to a one-off event. -. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. . Locate the {anomaly-job} whose model you want to revert in the job table. . Open the job details and navigate to the **Model Snapshots** tab. + diff --git a/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc b/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc index 68e152f65..c8d42ec4b 100644 --- a/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc +++ b/docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc @@ -1,6 +1,6 @@ tag::dfa-deploy-model[] . To deploy {dfanalytics} model in a pipeline, navigate to **Machine Learning** > -**Model Management** > **Trained models** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. +**Model Management** > **Trained models** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field] in {kib}. . Find the model you want to deploy in the list and click **Deploy model** in the **Actions** menu. diff --git a/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc b/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc index d9b77e77f..8335722b9 100644 --- a/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc +++ b/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc @@ -17,7 +17,7 @@ exception for your {kib} URL. -- -. Open *Machine Learning* from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +. Open *Machine Learning* from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. . Select the *{data-viz}* tab. diff --git a/docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc index 9b7e463cd..4ea26b878 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc @@ -92,7 +92,7 @@ NOTE: For most cases, the preferred version is the **Intel and Linux optimized** [[trained-model-e5]] ==== Using the Trained Models page -1. In {kib}, navigate to **{ml-app}** > **Trained Models** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. E5 can be found in +1. In {kib}, navigate to **{ml-app}** > **Trained Models** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. E5 can be found in the list of trained models. There are two versions available: one portable version which runs on any hardware and one version which is optimized for Intel® silicon. You can see which model is recommended to use based on your hardware @@ -250,7 +250,7 @@ xpack.ml.model_repository: file://${path.home}/config/models/` . Repeat step 2 and step 3 on all master-eligible nodes. . {ref}/restart-cluster.html#restart-cluster-rolling[Restart] the master-eligible nodes one by one. -. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. E5 can be found in the +. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field] in {kib}. E5 can be found in the list of trained models. . Click the **Add trained model** button, select the E5 model version you downloaded in step 1 and want to deploy and click **Download**. The selected diff --git a/docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc index 92bbefd01..d7da8a068 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc @@ -350,7 +350,7 @@ master-eligible nodes can reach the server you specify. . Repeat step 5 on all master-eligible nodes. . {ref}/restart-cluster.html#restart-cluster-rolling[Restart] the master-eligible nodes one by one. -. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. ELSER can be found in the +. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field] in {kib}. ELSER can be found in the list of trained models. . Click the **Add trained model** button, select the ELSER model version you downloaded in step 1 and want to deploy, and click **Download**. The selected @@ -390,7 +390,7 @@ xpack.ml.model_repository: file://${path.home}/config/models/` . Repeat step 2 and step 3 on all master-eligible nodes. . {ref}/restart-cluster.html#restart-cluster-rolling[Restart] the master-eligible nodes one by one. -. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. ELSER can be found in the +. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field] in {kib}. ELSER can be found in the list of trained models. . Click the **Add trained model** button, select the ELSER model version you downloaded in step 1 and want to deploy and click **Download**. The selected @@ -406,7 +406,7 @@ allocations and threads per allocation values. == Testing ELSER You can test the deployed model in {kib}. Navigate to **Model Management** > -**Trained Models** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. Locate the deployed ELSER model in the list of trained +**Trained Models** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field] in {kib}. Locate the deployed ELSER model in the list of trained models, then select **Test model** from the Actions menu. You can use data from an existing index to test the model. Select the index, diff --git a/docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc index ef1f8e37d..71fd063ee 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc @@ -18,7 +18,7 @@ can use it to perform {nlp} tasks in ingest pipelines. == Add an {infer} processor to an ingest pipeline In {kib}, you can create and edit pipelines in **{stack-manage-app}** > -**Ingest Pipelines**. To open **Ingest Pipelines**, find **{stack-manage-app}** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +**Ingest Pipelines**. To open **Ingest Pipelines**, find **{stack-manage-app}** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. [role="screenshot"] image::images/ml-nlp-pipeline-lang.png[Creating a pipeline in the Stack Management app,align="center"] diff --git a/docs/en/stack/ml/nlp/ml-nlp-ner-example.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-ner-example.asciidoc index 18dd8dc48..fd20f60c5 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-ner-example.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-ner-example.asciidoc @@ -294,7 +294,7 @@ You can create a tag cloud to visualize your data processed by the {infer} pipeline. A tag cloud is a visualization that scales words by the frequency at which they occur. It is a handy tool for viewing the entities found in the data. -In {kib}, open **Stack management** > **{data-sources-cap}** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar], and create a new +In {kib}, open **Stack management** > **{data-sources-cap}** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field], and create a new {data-source} from the `les-miserables-infer` index pattern. Open **Dashboard** and create a new dashboard. Select the diff --git a/docs/en/stack/ml/setup.asciidoc b/docs/en/stack/ml/setup.asciidoc index c8854e3e4..eb1ad5032 100644 --- a/docs/en/stack/ml/setup.asciidoc +++ b/docs/en/stack/ml/setup.asciidoc @@ -45,7 +45,7 @@ privileges are recommended if you control job level visibility via **Spaces**. You can configure these privileges - under **Security**. To open Security, find **{stack-manage-app}** in the main menu or -use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. - via the respective {es} security APIs. @@ -93,7 +93,7 @@ visualizations as well as {ml} job, trained model and module saved objects. In {kib}, the {ml-features} must be visible in your {kibana-ref}/xpack-spaces.html#spaces-control-feature-visibility[space]. To manage which features are visible in your space, go to **{stack-manage-app}** > -**{kib}** > **Spaces** or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] +**{kib}** > **Spaces** or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field] to locate **Spaces** directly. [role="screenshot"] @@ -101,12 +101,12 @@ image::spaces.jpg["Manage spaces in {kib}"] In addition to index privileges, source {data-sources} must also exist in the same space as your {ml} jobs. You can configure these under **{data-sources-caps}**. To open **{data-sources-caps}**, -find **{stack-manage-app}** > **{kib}** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +find **{stack-manage-app}** > **{kib}** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. Each {ml} job and trained model can be assigned to all, one, or multiple spaces. This can be configured in **Machine Learning**. To open **Machine Learning**, find **{stack-manage-app} > Alerts and Insights** in the main menu, -or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. +or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search field]. You can edit the spaces that a job or model is assigned to by clicking the icons in the **Spaces** column.