## Motivation for client-side deployment with TF.js The main motivation for a Python programmer, like me, to learn TF.js is to deploy the machine learning models I develop in TensorFlow into production in the user's browser, i.e. at the client-side. This production design has several benefits including— 1. Zero server costs: since the model is deployed on the client-side, there is no cost of running the model in a server. 2. Zero installation: the browser sets up the environment required to run the machine learning model. The user does not have to install anything to be able to run the model. The developer does not need to maintain a Docker image and interact with multiple languages. 3. Zero latency: once the model is loaded on a client's browser, the predictions are local and therefore there is no latency from sending messages to a remote server. In fact, the predictions can be made offline. 4. User privacy: since the user queries are not sent to a remote sever, it preserves user privacy. This can be particularly important in sensitive applications like in medicine and finance. 5. Scale: since the model travels to the user, it instantly scales to the demand. There is no need for server load balancing. 6. GDPR compliance: since the code travels and not the data, such a deployment automatically conforms to the [GDPR regulations](https://gdpr-info.eu/). 7. FAIR: such a deployment adheres to the [FAIR (findable, accessible, interoperable, and reusable) principles](https://arxiv.org/abs/2206.06159). The model is *findable* with a unique, persistent URL, where it is hosted. It is *accessible* via OAuth protocols or under some open source licensing. It is *interoperable* because any system that supports a browser can run the TF.js model. These factors make the model highly *reusable*. 8. Federated learning: such a design enables the development of [federated learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html) systems, where the code travels and not the data. 9. Prevalence: [JavaScript is by and far the most popular programming language](https://octoverse.github.com/#top-languages-over-the-years). Therefore, such a deployment can trigger rapid adoption and development of ideas.