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-# Machine Learning
-
-This component is an experimental machine learning local inference engine. Currently there is no inference engine actually integrated yet.
-
-Here is an example of the API:
-
-```js
-// The engine process manages the life cycle of the engine. It runs in its own process.
-// Models can consume large amounts of memory, and this helps encapsulate it at the
-// operating system level.
-const EngineProcess = ChromeUtils.importESModule("chrome://global/content/ml/EngineProcess.sys.mjs");
-
-// The MLEngineParent is a JSActor that can communicate with the engine process.
-const mlEngineParent = await EngineProcess.getMLEngineParent();
-
-
-/**
- * When implementing a model, there should be a class that provides a `getModel` function
- * that is responsible for providing the `ArrayBuffer` of the model. Typically this
- * download is managed by RemoteSettings.
- */
-class SummarizerModel {
-  /**
-   * @returns {ArrayBuffer}
-   */
-  static getModel() { ... }
-}
-
-// An engine can be created using a unique name for the engine, and the function
-// to get the model. This class handles the life cycle of the engine.
-const summarizer = mlEngineParent.getEngine(
-  "summarizer",
-  SummarizerModel.getModel
-);
-
-// In order to run the model, use the `run` method. This will initiate the engine if
-// it is needed, and return the result. The messaging to the engine process happens
-// through a MessagePort.
-const result = await summarizer.run("A sentence that can be summarized.")
-
-// The engine can be explicitly terminated, or it will be destroyed through an idle
-// timeout when not in use, as the memory requirements for models can be quite large.
-summarizer.terminate();
-```