Refine ML intro.

Author: szabosteve

explore-analyze/machine-learning.md

@@ -5,14 +5,59 @@ mapped_urls:
   - https://www.elastic.co/guide/en/serverless/current/machine-learning.html
 ---
 
-# Machine learning
+# What is Elastic Machine Learning? [machine-learning-intro]
 
-% What needs to be done: Align serverless/stateful
+{{ml-cap}} features analyze your data and generate models for its patterns of behavior.
+The type of analysis that you choose depends on the questions or problems you want to address and the type of data you have available.
 
-% Scope notes: include references to trained model autoscaling where appropriate
+## Unsupervised {{ml}} [machine-learning-unsupervised]
 
-% Use migrated content from existing pages that map to this page:
+There are two types of analysis that can deduce the patterns and relationships within your data without training or intervention: *{{anomaly-detect}}* and *{{oldetection}}*.
 
-% - [ ] ./raw-migrated-files/stack-docs/machine-learning/index.md
-% - [ ] ./raw-migrated-files/stack-docs/machine-learning/machine-learning-intro.md
-% - [ ] ./raw-migrated-files/docs-content/serverless/machine-learning.md
+[{{anomaly-detect-cap}}](machine-learning/anomaly-detection.md) requires time series data.
+It constructs a probability model and can run continuously to identify unusual events as they occur. The model evolves over time; you can use its insights to forecast future behavior.
+
+[{{oldetection-cap}}](machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md) does not require time series data.
+It is a type of {{dfanalytics}} that identifies unusual points in a data set by analyzing how close each data point is to others and the density of the cluster of points around it.
+It does not run continuously; it generates a copy of your data set where each data point is annotated with an {{olscore}}.
+The score indicates the extent to which a data point is an outlier compared to other data points.
+
+## Supervised {{ml}} [machine-learning-supervised]
+
+There are two types of {{dfanalytics}} that require training data sets: *{{classification}}* and *{{regression}}*.
+
+In both cases, the result is a copy of your data set where each data point is annotated with predictions and a trained model, which you can deploy to make predictions for new data.
+For more information, refer to [Introduction to supervised learning](machine-learning/data-frame-analytics/ml-dfa-overview.md#ml-supervised-workflow).
+
+[{{classification-cap}}](machine-learning/data-frame-analytics/ml-dfa-classification.md) learns relationships between your data points in order to predict discrete categorical values, such as whether a DNS request originates from a malicious or benign domain.
+
+[{{regression-cap}}](machine-learning/data-frame-analytics/ml-dfa-regression.md) learns relationships between your data points in order to predict continuous numerical values, such as the response time for a web request.
+
+## Feature availability by project type [machine-learning-serverless-availability]
+
+The {{ml-features}} that are available vary by project type:
+
+* {{es-serverless}} projects have trained models.
+* {{observability}} projects have {{anomaly-jobs}}.
+* {{elastic-sec}} projects have {{anomaly-jobs}}, {{dfanalytics-jobs}}, and trained models.
+
+## Synchronize saved objects [machine-learning-synchronize-saved-objects]
+
+Before you can view your {{ml}} {dfeeds}, jobs, and trained models in {{kib}}, they must have saved objects.
+For example, if you used APIs to create your jobs, wait for automatic synchronization or go to the **{{ml-app}}** page and click **Synchronize saved objects**.
+
+## Export and import jobs [machine-learning-export-and-import-jobs]
+
+You can export and import your {{ml}} job and {{dfeed}} configuration details on the **{{ml-app}}** page.
+For example, you can export jobs from your test environment and import them in your production environment.
+
+The exported file contains configuration details; it does not contain the {{ml}} models.
+For {{anomaly-detect}}, you must import and run the job to build a model that is accurate for the new environment.
+For {{dfanalytics}}, trained models are portable; you can import the job then transfer the model to the new cluster.
+Refer to [Exporting and importing {{dfanalytics}} trained models](machine-learning/data-frame-analytics/ml-trained-models.md#export-import).
+
+There are some additional actions that you must take before you can successfully import and run your jobs:
+
+* The {{data-sources}} that are used by {{anomaly-detect}} {dfeeds} and {{dfanalytics}} source indices must exist; otherwise, the import fails.
+* If your {{anomaly-jobs}} use custom rules with filter lists, the filter lists must exist; otherwise, the import fails.
+* If your {{anomaly-jobs}} were associated with calendars, you must create the calendar in the new environment and add your imported jobs to the calendar.