[ML] Trained models list: disable 'View training data' action if data frame analytics job no longer exists (#171061)

## Summary

Fixes https://github.com/elastic/kibana/issues/167667, disabling the
'View training data' action for models in the Trained Models list if the
data frame analytics job which created the model no longer exists

Adds `origin_job_exists` property to trained models list model items.
This is set during the models fetch for models with associated data
frame analytics jobs.

### Checklist

Delete any items that are not applicable to this PR.

- [ ] Any text added follows [EUI's writing
guidelines](https://elastic.github.io/eui/#/guidelines/writing), uses
sentence case text and includes [i18n
support](https://github.com/elastic/kibana/blob/main/packages/kbn-i18n/README.md)
- [ ]
[Documentation](https://www.elastic.co/guide/en/kibana/master/development-documentation.html)
was added for features that require explanation or tutorials
- [ ] [Unit or functional
tests](https://www.elastic.co/guide/en/kibana/master/development-tests.html)
were updated or added to match the most common scenarios
- [ ] Any UI touched in this PR is usable by keyboard only (learn more
about [keyboard accessibility](https://webaim.org/techniques/keyboard/))
- [ ] Any UI touched in this PR does not create any new axe failures
(run axe in browser:
[FF](https://addons.mozilla.org/en-US/firefox/addon/axe-devtools/),
[Chrome](https://chrome.google.com/webstore/detail/axe-web-accessibility-tes/lhdoppojpmngadmnindnejefpokejbdd?hl=en-US))
- [ ] If a plugin configuration key changed, check if it needs to be
allowlisted in the cloud and added to the [docker
list](https://github.com/elastic/kibana/blob/main/src/dev/build/tasks/os_packages/docker_generator/resources/base/bin/kibana-docker)
- [ ] This renders correctly on smaller devices using a responsive
layout. (You can test this [in your
browser](https://www.browserstack.com/guide/responsive-testing-on-local-server))
- [ ] This was checked for [cross-browser
compatibility](https://www.elastic.co/support/matrix#matrix_browsers)

---------

Co-authored-by: Kibana Machine <42973632+kibanamachine@users.noreply.github.com>

x-pack/plugins/ml/common/types/trained_models.ts

@@ -98,6 +98,7 @@ export type TrainedModelConfigResponse = estypes.MlTrainedModelConfig & {
    * Associated pipelines. Extends response from the ES endpoint.
    */
   pipelines?: Record<string, PipelineDefinition> | null;
+  origin_job_exists?: boolean;
 
   metadata?: {
     analytics_config: DataFrameAnalyticsConfig;

x-pack/plugins/ml/public/application/model_management/model_actions.tsx

@@ -130,18 +130,19 @@ export function useModelActions({
   return useMemo(
     () => [
       {
-        name: i18n.translate('xpack.ml.trainedModels.modelsList.viewTrainingDataActionLabel', {
+        name: i18n.translate('xpack.ml.trainedModels.modelsList.viewTrainingDataNameActionLabel', {
           defaultMessage: 'View training data',
         }),
         description: i18n.translate(
           'xpack.ml.trainedModels.modelsList.viewTrainingDataActionLabel',
           {
-            defaultMessage: 'View training data',
+            defaultMessage: 'Training data can be viewed when data frame analytics job exists.',
           }
         ),
         icon: 'visTable',
         type: 'icon',
         available: (item) => !!item.metadata?.analytics_config?.id,
+        enabled: (item) => item.origin_job_exists === true,
         onClick: async (item) => {
           if (item.metadata?.analytics_config === undefined) return;
 
@@ -164,7 +165,6 @@ export function useModelActions({
 
           await navigateToUrl(url);
         },
-        isPrimary: true,
       },
       {
         name: i18n.translate('xpack.ml.inference.modelsList.analyticsMapActionLabel', {

x-pack/plugins/ml/public/application/model_management/models_list.tsx

@@ -79,6 +79,7 @@ export type ModelItem = TrainedModelConfigResponse & {
   type?: string[];
   stats?: Stats & { deployment_stats: TrainedModelDeploymentStatsResponse[] };
   pipelines?: ModelPipelines['pipelines'] | null;
+  origin_job_exists?: boolean;
   deployment_ids: string[];
   putModelConfig?: object;
   state: ModelState;

x-pack/plugins/ml/server/routes/trained_models.ts

@@ -29,9 +29,9 @@ import {
   createIngestPipelineSchema,
   modelDownloadsQuery,
 } from './schemas/inference_schema';
-import type {
+import {
   PipelineDefinition,
-  TrainedModelConfigResponse,
+  type TrainedModelConfigResponse,
 } from '../../common/types/trained_models';
 import { mlLog } from '../lib/log';
 import { forceQuerySchema } from './schemas/anomaly_detectors_schema';
@@ -39,10 +39,9 @@ import { modelsProvider } from '../models/model_management';
 
 export const DEFAULT_TRAINED_MODELS_PAGE_SIZE = 10000;
 
-export function filterForEnabledFeatureModels(
-  models: TrainedModelConfigResponse[] | estypes.MlTrainedModelConfig[],
-  enabledFeatures: MlFeatures
-) {
+export function filterForEnabledFeatureModels<
+  T extends TrainedModelConfigResponse | estypes.MlTrainedModelConfig
+>(models: T[], enabledFeatures: MlFeatures) {
   let filteredModels = models;
   if (enabledFeatures.nlp === false) {
     filteredModels = filteredModels.filter((m) => m.model_type === 'tree_ensemble');
@@ -191,10 +190,38 @@ export function trainedModelsRoutes(
             mlLog.debug(e);
           }
 
-          const body = filterForEnabledFeatureModels(result, getEnabledFeatures());
+          const filteredModels = filterForEnabledFeatureModels(result, getEnabledFeatures());
+
+          try {
+            const jobIds = filteredModels
+              .map((model) => {
+                const id = model.metadata?.analytics_config?.id;
+                if (id) {
+                  return `${id}*`;
+                }
+              })
+              .filter((id) => id !== undefined);
+
+            if (jobIds.length) {
+              const { data_frame_analytics: jobs } = await mlClient.getDataFrameAnalytics({
+                id: jobIds.join(','),
+                allow_no_match: true,
+              });
+
+              filteredModels.forEach((model) => {
+                const dfaId = model?.metadata?.analytics_config?.id;
+                if (dfaId !== undefined) {
+                  // if this is a dfa model, set origin_job_exists
+                  model.origin_job_exists = jobs.find((job) => job.id === dfaId) !== undefined;
+                }
+              });
+            }
+          } catch (e) {
+            // Swallow error to prevent blocking trained models result
+          }
 
           return response.ok({
-            body,
+            body: filteredModels,
           });
         } catch (e) {
           return response.customError(wrapError(e));