Machine Learning has come a long way in the past decade. With differentiable programming we have seen a renewed interest from numerous communities to apply ML techniques to diverse fields through scientific machine learning. Traditional deep learning has seen many strides with larger, more compute-intensive models which need increasingly complex training routines that push the boundaries of the current state-of-the-art.

In this talk, we will go through the depth of the machine learning and differentiable programming ecosystem in Julia through the FluxML stack. We shall discuss the various tools and features available to the users through the advances in the ecosystem and the next-gen tooling required to allow even more expressive modelling possible in Julia.

We will also take note of the new packages and techniques being developed in domains such as differentiable physics, chemistry, graph networks, molecular simulation and multi-GPU training etc.

We will also talk about the development effort in the Flux stack including performance enhancements, better coverage of CUDA, NNlib optimisations for the CPU and the new composable and functional optimisers via Optimisers.jl etc.