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.
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.