Pytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.
I would recommend it for use when anyone wants to quickly develop a neural network. Or if a user is solving any machine learning problem that includes deep learning. And this kind of problem will be like image recognition, face recognition, doing some text analysis using deep learning which includes LSTM or some other algorithm.
Everything deep learning related if not on TPU (in such case, JAX would be better suited). For LLM deployment, libraries such as vLLM would be better suited, too; otherwise, wrapping the PyTorch model with Ray is a good option.
The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.
As Keras is the high level API, so using Keras, we don't have to be bothered by the low level TensorFlow complexity, and we can reduce a lot coding and testing efforts.
Saving and loading Machine/Deep Learning models is very easy with Pytorch. It provides visualization capabilities when combined with Tensorboard, and mathematical operations are highly optimized. Easy to understand for a person who is an expert in Python. It takes significantly less time to create valuable POCs as most of the things are inbuilt.
