Some of the most cutting edge and effective web performance optimizations, like prefetch and preconnect, involve being proactive. We make predictions to determine where a user is likely to go next and load resources ahead of time so page load is as fast as possible. While there is somewhat of a science to making these predictions, thanks to analytics tracking and extensive user testing, they are still largely manual. Making an accurate prediction therefore becomes progressively more difficult as your site scales since there’s no sure way of knowing where a user will go next. This is where machine learning comes in handy. By training a machine learning model (maybe a markov chain?) with current analytics data, we can take the guess work our of our predictions and more accurately load resources ahead of time. There’s currently ongoing development to make techniques such as this more accessible to web developers. A project worth checking out is called GuessJS, which serves as a landing page for all libraries and tools that enable Machine Learning driven user-experiences on the web. The convergence of machine learning and web performance is still in its infancy. However, with the growing popularity of machine learning (especially among web developers), it’s only a matter of time when techniques like predictive prefetching will become more commonplace on the web.