Accelerating Your Algorithms in Production with Python and Intel MKL

Numerical algorithms are computationally demanding, which makes performance an important consideration when using Python for machine learning, especially as you move from desktop to production.

In this webinar, we look at:

  • Role of productivity and performance for numerical computing and machine learning
  • Python algorithm choice and efficient package usage
  • Requirements for efficient use of hardware
  • NumPy and SciPy performance with the Intel MKL (math kernel library)
  • How Intel and ActivePython help you accelerate and scale Python performance

 

Time to watch: 58 min

Presenters:
Sergey Maidanov, Software Engineering Manager, Intel
Tom Radcliffe, VP Engineering, ActiveState



Mike Kanasoot

Mike Kanasoot

Mike is the Web Marketing Manager at ActiveState. He has worked in industries ranging from security and document management to mobile commerce, but enjoys the culture of open source technology in particular. As a marketer, Mike believes in providing great user experiences and tracking everything.