Using Julia for standard ML stuff like images and NLP is a stretch if prepackaged algorithms exist for python frameworks. But using AD packages for scientific machine learning and the million other algorithms that require gradients (eg, bayesian estimation and probabilistic programming) with flexibility in creating the computational graph is where Julia should have a sweet spot. Hopefully significant investment in AD can make that a reality.
Shout out to the those doing zygote maintainenance the best they can (@ToucheSir and @mcabbott and @devmotion and many others), and acknowledging the thankless task to maintain something they didn’t design, with perplexing interactions with the compiler, and grumpy users.