Conventional methods of uploading machine learning models for web applications can be a daunting task for web developers who specialize in Javascript. Learning Python, deployment of models, cross referencing machine learning models made in Python using APIs in Nodejs backend environment are some of the additional requirements for a web developer with little or no experience in Python or Machine Learning. The developers might require powerful CPUs/GPUs to be used for training of the models. This is where Tensorflow.JS ( TFJS ) comes into the picture. It allows standard machine learning libraries and models to be used directly with Javascript. It runs the models on the Browser ( client side ), or on the backend with Nodejs. And it makes it really easy for the Javascript developers to integrate machine learning models without much knowledge behind how these models work. But to give more flexibility TensorflowJS Converter allows Python Machine learning Models to be converted to Javascript nodejs based models.

I will be talking about TFJS Converter, the inner architecture and functioning of it. I will showcase certain code samples for TFJS Converter and will also discuss on how to contribute to open sourced TFJS and TFJS Converter.

Outline : - Brief intro about TensorflowJS - Brief discussion on standard ways of deploying python Machine learning models with Flask and Django in a web application - Introduction about TensorflowJS Converter - Architecture of TFJS Converter - Practical demo of steps to be followed for using TensorflowJS Converter - Few demo Python ML models converted to Nodejs Backend using TFJS Converter and ending notes