End-to-end analytics is among the biggest challenges for those in the data sciences realm—from machine-learning developers and data scientists to numerical and scientific computing developers. To help, Intel has created data analytics and machine-learning pipelines with the Intel® Distribution for Python*.
Tune in to this session to watch lead Python* technical consulting engineer, David Liu discuss these pipelines, including:
- How to get close-to-native performance with Intel-optimized, compute-intense packages like NumPy, SciPy, and scikit-learn*
- Get high performance and scalability from multiple cores on a single machine, as well as large clusters of workstations
- Achieve performance and scalability similar to hand-tuned C++ and message passing interface (MPI) codes while using the known productivity of Python
David also includes many examples.
Get the Software
Download the Intel® Distribution for Python*
Technical consulting engineer, Intel Corporation
David Liu specializes in open-source software development and focuses on machine learning, deep learning, artificial intelligence (AI), software architecture, and build infrastructure. In his current role, he is responsible for assisting customers and the open-source community in all phases of improving software quality and optimizing it for Intel® hardware. David joined Intel in 2015 and holds a masters of science degree in software engineering from the University of Texas, Austin.