Beyond that, there are hundreds of open source projects you can fork to start building intuitions around what the inner loop of AI dev project cycles look like. You'll be surprised at how much of your web dev skills remain relevant in these projects, particularly in UI-related tasks. Data scientists and folks of similar ilk default to notebooks, gradio, streamlit et al. to ship interfaces for their experiments. You though have the ability to do that on your own if you choose to (sometimes a notebook is enough), which can be a valuable differentiator for you as a candidate if you also have all the other skills needed to be productive in this space.
My own background is in distributed systems with some full stack and embedded work mixed in over the years. I started tinkering with ML projects back in 2012 when I first discovered AlexNet and resources were far more limited. I was still able to get productive relatively quickly even though most of what I built wasn't really applicable to my work in a practical sense on day one. Where my background became relevant was when I needed something approximating an MLOps pipeline for data processing, training, and eval. Most of the code you're writing for that isn't really specific to ML, its nearly identical to CI/CD systems but with the obvious infrastructure caveats native to ML workloads.
Nowadays though, especially if you're intrepid/resourceful, there is so much more learning material by comparison and much of what you work on can likely augment your day-to-day web dev tasks as well if you're creative enough.