An updated and revised discussion of different deployment options

PiperOrigin-RevId: 225602860

site/en/deploy/_toc.yaml

@@ -1,16 +1,7 @@
 toc:
 - title: Deploy
-  path: /deploy/
-- title: TensorFlow Serving
-  path: /serving
-- title: TensorFlow Extended (TFX)
-  path: /tfx
-- title: Deploy to JavaScript
-  path: https://js.tensorflow.org/
-  status: external
-- title: Deploy to Mobile
-  path: /lite
-- section: Other Deployment Scenarios
+  path: /deploy
+- heading: Other Deployment Scenarios
 - title: Distributed TensorFlow
   path: /deploy/distributed
 - title: TensorFlow on Hadoop

site/en/deploy/index.md

@@ -32,12 +32,12 @@ perform A/B testing. With TFX, your models are ready for production.
 ## TensorFlow.js
 
 With [TensorFlow.js](https://js.tensorflow.org/) you can train and deploy your
-TensorFlow models in the browser and in Node.js. Use flexible and intuitive APIs
-to build and train models from scratch using the low-level JavaScript linear
-algebra library or the high-level layers API. Use TensorFlow.js model converters
-to run pre-existing TensorFlow models right in the browser or under Node.js.
-Retrain pre-existing ML models using sensor data connected to the browser, or
-other client-side data.
+TensorFlow models in the browser and in Node.js. Use flexible and intuitive
+APIs to build and train models from scratch using the low-level JavaScript
+linear algebra library or the high-level layers API. Use TensorFlow.js model
+converters to run pre-existing TensorFlow models right in the browser or under
+Node.js. Retrain pre-existing ML models using sensor data connected to the
+browser, or other client-side data.
 
 ## TensorFlow Lite