Documents the model memory estimation in the edit job flyout

[nastasha-solomon]

Summary

Documents the Apply estimation option in the Kibana edit job flyout for anomaly detection and data frame analytics jobs to fix #6495.

Previews

• Working with anomaly detection at scale | Set the model memory limit - Updated guidance on adjusting model_memory_limit for existing jobs to mention the Apply option in the edit job flyout on the Anomaly Detection Jobs page. Updated the tip to lead with the UI option before the API fallback.

• Run a job | Model memory limits - Added a paragraph surfacing Apply in the edit job flyout. This is the primary landing page for users hitting memory issues, so it's the right place to mention the shortcut before they resort to recreating the job.

• Data frame analytics limitations - Made two changes:

  • Corrected an overbroad limitation that said DFA configurations can't be updated. It now accurately states that most settings can't be changed, but model_memory_limit can using the API or Apply in the edit job flyout on the Data Frame Analytics page.
  • Expanded the memory limitation section with actionable guidance on how to estimate memory and how to fix a job that fails to start due to insufficient memory.

• Working with data frame analytics at scale | 7. Set the model memory limit - New section explaining what model_memory_limit controls, how to update it via the API or Apply in the edit job flyout, and how to get a memory estimate. Modeled after the equivalent section on the anomaly detection at scale page.

Generative AI disclosure

1. Did you use a generative AI (GenAI) tool to assist in creating this contribution?

• Yes - Cursor + Auto
• No

[github-actions]

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[github-actions]

🔍 Preview links for changed docs

• explore-analyze/machine-learning/anomaly-detection/anomaly-detection-scale.md
• explore-analyze/machine-learning/anomaly-detection/ml-ad-run-jobs.md
• explore-analyze/machine-learning/data-frame-analytics/ml-dfa-limitations.md
• explore-analyze/machine-learning/data-frame-analytics/ml-dfa-scale.md

[github-actions]

Vale Linting Results

Summary: 1 suggestion found

💡 Suggestions (1)

File	Line	Rule	Message
explore-analyze/machine-learning/anomaly-detection/ml-ad-run-jobs.md	113	Elastic.WordChoice	Consider using 'select, press, visits' instead of 'hit', unless the term is in the UI.

---

The Vale linter checks documentation changes against the Elastic Docs style guide.

To use Vale locally or report issues, refer to Elastic style guide for Vale.

[github-actions]

Docs review summary

Focus areas

• Style and clarity: Changes are generally clear and well-written. Found one minor grammar issue and one instance of redundant wording. The Vale linting comment already flagged appropriate word-choice issues.
• Jargon: No Elastic-internal jargon issues found. UI labels and technical terms are appropriately used in context.
• Frontmatter and applies_to: All four files have proper frontmatter with required products, applies_to, and mapped_pages fields. The new applies_to tags for 9.5+ features follow the correct cumulative docs pattern.
• Content type fit: Changes maintain appropriate structure for each page type (scale guides, how-to, and limitations reference).
• Parent issue satisfaction: Satisfied. The PR successfully documents the Apply estimation option in the edit job flyout for both anomaly detection and data frame analytics jobs as requested in issue #6495.

Nits

• Line 113 of ml-ad-run-jobs.md uses "hit" where style guide prefers "reach" or "exceed" (already flagged by Vale).
• Line 86 of anomaly-detection-scale.md: "However, you must provide your own cardinality estimates when using the API" could flow better as "However, when using the API, you must provide your own cardinality estimates."

Generated by Docs review agent for issue #6592 · ● 781K

[github-actions]

Grammar issue with "due to the model is" construction. Use "due to" with a noun phrase or gerund:

Suggested change

	When you create a {{dfanalytics-job}} and the inference step of the process fails due to the model is too large to fit into JVM, follow the steps in [this GitHub issue](https://github.com/elastic/elasticsearch/issues/76093) for a workaround.
	When you create a {{dfanalytics-job}} and the inference step of the process fails due to the model being too large to fit into JVM, follow the steps in [this GitHub issue](https://github.com/elastic/elasticsearch/issues/76093) for a workaround.

[github-actions]

The phrase "in the edit job flyout" appears twice in this sentence, making it redundant. Consider simplifying:

Suggested change

You can update `model_memory_limit` on a stopped job using the [update {{dfanalytics-jobs}} API]({{es-apis}}operation/operation-ml-update-data-frame-analytics). {applies_to}`stack: ga 9.5`{applies_to}`serverless: ga` In {{kib}}, navigate to the **Data Frame Analytics** page, open the edit flyout for the stopped job, and select **Apply** under the **Model memory limit** field in the edit job flyout.

You can update `model_memory_limit` on a stopped job using the [update {{dfanalytics-jobs}} API]({{es-apis}}operation/operation-ml-update-data-frame-analytics). {applies_to}`stack: ga 9.5`{applies_to}`serverless: ga` In {{kib}}, navigate to the **Data Frame Analytics** page, open the edit flyout for the stopped job, and select **Apply** under the **Model memory limit** field.