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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();
```
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