| Redis is an in-memory data store and can function as a database, cache, or message broker. Redis supports a wider variety of data types and structures than etcd and has much faster read/write performance.
* Magenta: An open source research project exploring the role of machine learning as a tool in the creative process. https://magenta.tensorflow.org/[url^]
PyTorch doesn't provide any framework to deploy models directly on to the web. You'll have to use either Flask or Django as the backend server.
| *Production Deployment*
When it comes to deploying trained models to production, TensorFlow is the clear winner. We can directly deploy models in TensorFlow using TensorFlow serving which is a framework that uses REST Client API.
|*Visualization*
PyTorch developers use Visdom, the features provided by Visdom are very minimalistic and limited.
|*Visualization*
When it comes to visualization of the training process, TensorFlow takes the lead. Visualization helps the developer track the training process and debug in a more convenient way.
TenforFlow’s visualization library is called TensorBoard.
|Python
|Tensorflow has APIs for various langages :
* C (used to develop other APIs in practice, though)
=== Reasons to learn machine learning with JavaScript
====
*Private machine learning*
Most machine learning applications rely on client-server architectures. Users must send their data where the machine learning models are running.
Machine learning models on your device without sending any data to the cloud.
And you don’t need to install any additional software.
Powerful JavaScript machine learning libraries:
* https://ml5js.org/[ml5js^]
* http://caza.la/synaptic/#/[synaptic^]
* https://brain.js.org/#/[brain^]
*Easy integration of machine learning in web and mobile applications*
Python support in mobile operating systems is still in the preliminary stages.
Javascript has a rich set of cross-platform mobile app development tools such as Cordova and Ionic
TIP: Machine learning with Node.js is fairly new, but it is fast evolving because there is growing interest in adding machine learning capabilities to web and mobile applications.
=== Reasons to learn machine learning with Python - PyTorch
====
*Python is better suited for server-side training of machine learning models*
It can scale and distribute its load on server clusters to accelerate the training process. Once the model is trained, you can compress it and deliver it on user devices for inference. Fortunately, machine learning libraries written in different languages are highly compatible.
For instance, if you train your deep learning model with TensorFlow or Keras for Python, you can save it in one of several language-independent formats such as JSON or HDF5. You can then send the saved model to the user’s device and load it with TensorFlow.js or another JavaScript deep learning library.
