Adoption of Python has been enormous over the last decade. Why? It’s easy to use, accessible, versatile and can be used for AI, machine learning, data analytics, data visualization, and all types of science. Tune into how 3 experts are involved in building common standards for Python data APIs that can help users use a large collection of libraries to develop high-performance code without limitations on adding exotic features. Regarding the huge growth of custom libraries, “it was more a social problem than a technical problem…we should avoid writing more code. Writing yet another library is very likely not going to be the answer.” The Python Data API Consortium was organized to bring people together and solve a fundamental problem to deliver a common standard. Listen in. [26:32]

Guests:
Travis Oliphant, Quansight’s CEO and the creator of SciPy, NumPy, and Numba; founder/director of Anaconda, and founder of NumFOCUS
Ralf Gommers, co-director of Quansight Labs and director of the Python Data API Standards consortium
Areg Melik-Adamyan, principal engineer and engineering manager at Intel

To learn more
Python Data API Consortium (data-apis.org)
Intel oneAPI AI Analytics Toolkit

Read/Download the transcript

Tags: C++, developers, Intel, Intel Distribution of Python, Intel oneAPI AI Analytics Toolkit, NumPy, oneAPI, Pandas, Python, Python Data API Consortium, Python libraries, Pytorch, scikit-learn, SciPy, TensorFlow
 
Posted in: Code Together, Intel