Although they are incredibly useful, the ML applications, or MLops, or ML Devops, or whatever other production-related application tech stack terminology may co

Although they are incredibly useful, the ML applications, or MLops, or ML Devops, or whatever other production-related application tech stack terminology may come to mind, are providing critical scaffolding infrastructure and glue but are not strictly what comes to mind when you only use the term “Machine Learning”. The key to machine learning is the massively parallel ability to efficiently train large neural network models, and the key to using the benefits of these trained models is the ability to rapidly evaluate them. Yes you need to get data for the training and you need complex infrastructure for the applications but the conferences, papers, studies on machine learning dont refer to these (other than in passing), in part because they are solvable, and in part because they are largely orthogonal to ML. Another way to think about it is that the nvidia price is what goes to infinity in this recent run and not the database or disk drive providers. So if someone finds a way to make the core ML part better with a programming language solution that is certainly very welcome and the title is appropriate. (The fact that GPU programming is considered niche in the current state of ML is a strong argument for keeping the title as is.)