Speed-Up Python* Applications & Soar Core Computations

Python* is among the most popular programming languages due to its ease-of-use for cloud-based Web applications and out-of-the-box performance.

But.

It also has a reputation for executing scripts slower than its native equivalents due to its single-threaded nature, which provides language flexibility at the cost of performance.

Intel® Distribution for Python* overcomes this challenge, offering optimized numeric, scientific, and machine learning packages to accelerate Python performance on a wide range of Intel® processors.

Watch to learn how the Intel distribution helps Data Scientists enable high-performance computing in Python and Machine Learning applications, including:

  • An overview of the Python performance packages
  • Methods to speed up Python performance
  • Intel-optimized scikit-learn performance speedups
  • Deep-dives on Intel® Data Analytics Acceleration Library in Python (PyDAAL)
  • Demos with examples
Preethi Venkatesh, Technical Consultant Engineer, Intel Corporation

Preethi joined Intel in 2017, and works with the Intel Software and Services Group, driving customer adoption of Intel® Distribution for Python* and Intel® Data Analytics Acceleration Library through training, article publications and open-source contributions, and has published multiple software tools white papers. Before joining Intel, she was a Business Data Analyst at Infosys Limited for four years. Preethi has a Bachelor’s Degree in Instrumentation Technology/Technician from Visvesvaraya Technological University, Belgaum, India, and a Master’s Degree in Information Systems on Data Science from the University of Texas at Arlington.

Performance varies by use, configuration, and other factors. Learn more at www.Intel.com/PerformanceIndex.