Part 12/12:
The presenter urged practitioners to start experimenting with JAX, especially given its compatibility with popular frameworks like Flax (built on top of JAX), which simplifies neural network construction. They advised that organizations seeking maximum speed and scalability should seriously consider adopting JAX, but also cautioned to weigh the migration efforts for established codebases.
In essence, JAX represents a paradigm shift in machine learning computation: combining the power of Python, the speed of custom C++ kernels, and the flexibility of functional programming, all wrapped into an accessible and performant package.