This chapter examines the optimization of Python's performance in data science, focusing on data structures and integrating NVIDIA's CUDA for GPU programming. It also highlights projects, libraries, and techniques that enhance performance, detailing the roles of Numba, PyTorch, and vector databases.
Python performance has come a long way in recent times. And it's often the data scientists, with their computational algorithms and large quantities of data, who care the most about this form of performance. It's great to have Stan Seibert back on the show to talk about Python's performance for data scientists. We cover a wide range of tools and techniques that will be valuable for many Python developers and data scientists.
Episode sponsors
Posit
Talk Python Courses
Links from the show