Parallel processing in Python: The current landscape
Python has a vast ecosystem of tools for scientific computing and data science. However, when data size or computational complexity grows, users may encounter performance challenges. This talk will cover the current landscape of parallel processing tools in Python, with a focus on which tools are best suited for various workloads such as arrays, dataframes, machine learning, and deep learning.
Aaron Richter is a software developer turned data engineer and data scientist. He has pioneered the development and implementation of large-scale data science infrastructure in both business and research environments. Inevitably, he spent a lot of time finding efficient ways to clean data, run pipelines, and tune models. Aaron is a Senior Data Scientist at Saturn Cloud, and holds a PhD in machine learning from Florida Atlantic University.