Machine learning is no longer merely a buzz word. It’s matured into a major disruptor, profoundly impacting business and transforming how we interact in the world and with each other.

For software developers, it’s becoming a major differentiator that leads to the obvious question: How do you increase your machine-learning performance?

One way is by using the Intel® Distribution for Python*—a set of accelerated numeric Python* packages, including scikit-learn*, NumPy, SciPy, and daal4py, all optimized to work in both single and distributed modes.

Join David Liu, technical consulting engineer at Intel, for an overview of Intel Distribution for Python and the daal4py package, including:

  • How they can decrease your machine-learning compute time
  • Where and when to use daal4py in your machine-learning application
  • The new High-Performance Analytics Toolkit (HPAT) feature and how it accelerates getting data into your machine-learning application

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David Liu
Technical consulting engineer, Intel Corporation

David Liu specializes in open-source software development and focuses on machine learning, deep learning, 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.

 

Intel® Distribution for Python*

Develop fast, performant Python code with this set of essential computational packages including NumPy, SciPy, scikit-learn*, and more.

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