May 10, 2016

Python has emerged as one of the most popular languages in data science due to its open source nature, easy-to-learn syntax and active developer community. However, the data science industry is moving at warp speed and deadlines are becoming shorter, while data sizes are increasing. Many data scientists struggle to achieve necessary performance using Python with their existing infrastructure.

We are here to help! On May 10th, Dr. Stan Seibert, High Performance Analytics Team Lead from Continuum Analytics, will join forces with Sergey Maidanov, Software Manager from Intel Corporation, to explore both software and hardware technologies to boost the performance of your Python applications.

Scale Your Data Science by Maximizing Your Existing Investment…Before Jumping to Clusters

Python has emerged as one of the most popular languages in data science due to its open source nature, easy-to-learn syntax and active developer community. 

However, the data science industry is moving at warp speed and deadlines are becoming shorter, while data sizes are increasing. Many data scientists struggle to achieve necessary performance using Python with their existing infrastructure.

We are here to help! On May 10th, Dr. Stan Seibert, High Performance Analytics Team Lead from Continuum Analytics, will join forces with Sergey Maidanov, Software Manager from Intel Corporation, to explore both software and hardware technologies to boost the performance of your Python applications. They will demonstrate how the Anaconda platform, combined with Intel technology, can turbocharge the performance of your Python data science applications. We will also discuss how to streamline the optimization process with code profiling, Intel® Math Kernel Library (Intel® MKL) and Intel® Data Analytics Acceleration Library (Intel® DAAL), as well as the compilation of Python with the Numba compiler.

In this webinar, you’ll learn to:

  • Understand the high performance Python options for Scale Up and Scale Out
  • Understand the choices of performance strategies to achieve performance increases from 10X to 100X
  • Use optimized libraries, such as Intel® MKL and DAAL, with high performance hardware
  • Speed up Python numerical analysis with Numba 

Stan and Sergey will be hosting a Q and A session after the presentation, so tune in and get your questions answered.