TemporalGPs.jl provides a single-function API to make inference in Gaussian processes (GPs) from Stheno.jl dramatically more efficient in the time-series setting. It makes it feasible to perform (almost) exact inference with 10s of millions of data points quickly on a laptop, and plays nicely with StaticArrays.jl and Zygote.jl. Gaussian processes (GPs) are flexible probabilistic models for real-valued functions, and are standard tool for nonlinear regression. Their default implementation involves a costly Cholesky decompostion, yielding cubic computational costs in N (dataset size). There is an increasingly-broad literature on a collection of techniques that cast certain classes of GPs for time-series (almost-exactly) as linear stochastic differential equations. This exposes a Markov structure that can be exploited to make the complexity of inference scale linearly in N. TemporalGPs.jl implements the above techniques, and provides a function that takes a Stheno.jl GP and constructs an (almost) equivalent linear SDE, which in turn exposes the same API as a Stheno.jl GP (modulo some restrictions). The only other publicly available implementation of these techniques is available in the famous GPML Matlab package. Moreover, at the time of writing there isn't a publicly available implementation on these techniques that can be used in conjunction with reverse-mode algorithmic differentiation (to the best of the author's knowledge). In this talk I'll present the user-facing API, show some basic benchmarking / empirical results, briefly discuss some technical challenges, and conclude with the package's future direct. Time Stamps: 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/YouTubeVideoTimestamps Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/YouTubeVideoSubtitles