3 reasons: 1. Pytorch is getting better at deployment. The only reason to use TF until recently was its superior deployment capabilities. 2. Google itself fin

3 reasons: 1. Pytorch is getting better at deployment. The only reason to use TF until recently was its superior deployment capabilities. 2. Google itself finally realized what a clusterfuck TF has been, threw in the towel, and started fresh with Jax. 3. Pytorch is king in research. ML is still very much a research driven field, so whatever researchers choose is going to win sooner or later. Jax is a serious contender though. Google invested a lot in TF development, and the inertia will keep it alive for a couple more years, but the writing on the wall is clear.