Traditionally, AI models are trained and evaluated in specific high-performance or cloud environments. Currently, there is a trend towards edge computing which is primarily caused by better processors and hardware acceleration, especially on mobile devices. Furthermore, data protection is becoming ever more important and running AI on the client-side, without data leaving the device, becomes increasingly relevant. With Tensorflow.js models can be trained and evaluated in the browser or in the backend. This enables web applications to use AI online as well as offline. Moreover, there is vendor independent hardware acceleration with WebGL when using the browser. That means no lock-in on CUDA and better performance than running solely on the CPU. In a practical example the complete workflow for developing a gesture classifier will be presented. Participants will gain insights into the Tensorflow.js API and learn how to apply transfer learning. For demonstrative purposes, the classifier will be integrated into a vertical scrolling airplane game. Training and evaluation will be done completely in the browser, so that no data leaves the device. Learning objectives: Build models that can be used in the frontend and backend, understand the power and limitations of transfer learning and understand concepts of modern computer vision models.