The text discusses the differences between two popular deep learning frameworks, PyTorch and TensorFlow. It covers practical considerations such as model availability, deployment infrastructure, and ecosystems. While both frameworks have their strengths, the article suggests that TensorFlow currently has a more robust deployment framework and superior ecosystem, making it the preferred choice for industry applications. However, PyTorch is the de facto research framework due to its wide adoption by the research community. The text also provides recommendations on which framework to use based on specific use-cases or goals.