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Elastic Docs ›Machine Learning in the Elastic Stack [7.11] ›Data frame analytics ›Overview

How a data frame analytics job works

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How a data frame analytics job works

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This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features.

A data frame analytics job is essentially a persistent Elasticsearch task. During its life cycle, it goes through four or five main phases depending on the analysis type:

  • reindexing,
  • loading data,
  • analyzing,
  • writing results,
  • inference (regression and classification only).

Let’s take a look at the phases one-by-one.

Reindexing

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During the reindexing phase the documents from the source index or indices are copied to the destination index. If you want to define settings or mappings, create the index before you start the job. Otherwise, the job creates it using default settings.

Once the destination index is built, the data frame analytics job task calls the Elasticsearch Reindex API to launch the reindexing task.

Loading data

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After the reindexing is finished, the job fetches the needed data from the destination index. It converts the data into the format that the analysis process expects, then sends it to the analysis process.

Analyzing

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In this phase, the job generates a machine learning model for analyzing the data. The specific phases of analysis vary depending on the type of data frame analytics job.

Outlier detection jobs have a single analysis phase called computing_outliers, in which they identify outliers in the data.

Regression and classification jobs have four analysis phases:

  1. feature_selection: Identifies which of the supplied fields are most relevant for predicting the dependent variable.
  2. coarse_parameter_search: Identifies initial values for undefined hyperparameters.
  3. fine_tuning_parameters: Identifies final values for undefined hyperparameters. See hyperparameter optimization.
  4. final_training: Trains the machine learning model.

Writing results

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After the loaded data is analyzed, the analysis process sends back the results. Only the additional fields that the analysis calculated are written back, the ones that have been loaded in the loading data phase are not. The data frame analytics job matches the results with the data rows in the destination index, merges them, and indexes them back to the destination index.

Inference

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This phase exists only for regression and classification jobs. In this phase, the job validates the trained model against the test split of the data set.

Finally, after all phases are completed, the task is marked as completed and the data frame analytics job stops. Your data is ready to be evaluated.

Check the Concepts section if you’d like to know more about the various data frame analytics types.

Check the Evaluating data frame analytics section if you are interested in the evaluation of the data frame analytics results.

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