Accelerating Your Algorithms in Production with Python and Intel MKL, Sep 21

We will provide tips for data scientists to speed up Python algorithms, including a discussion on algorithm choice, and how effective package tool can make large differences in performance.

Sponsored Post. Activestate Intel Webinar 2017 Sep 21
Webinar: Accelerating Your Algorithms in Production with Python and Intel MKL

Date and time: Thu, Sep 21, 2017 10:30 am PT, 1:30 pm ET

Numerical algorithms are computationally demanding, which makes performance an important consideration with the use of Python for machine learning. Rolling out Python algorithms from a desktop prototype environment to a production environment with many nodes and magnitudes more data, is a challenge.

In this webinar, we will provide tips for data scientists to speed up Python algorithms. First a discussion on algorithm choice, and understanding how effective package tool usage can make large differences in performance gains.

Then, we will demonstrate how Intel accelerates Python for numerical computing and machine learning by seeing how Intel performance libraries such as Intel MKL accelerate basic linear algebra operations and solvers, FFTs, arithmetic and transcendental operations.

You will also get a behind-the- scenes look at how Intel engineers have optimized Python to scale from Intel Atom or Intel Core based laptops to powerful Intel Xeon and Xeon Phi based clusters to achieve faster performance.

Register Now.


Sergey Maidanov, Software Engineering Manager, Intel
Sergey Maidanov leads a team of software engineers working on the optimized Intel® Distribution for Python. He has 15+ years of experience in numerical analysis with a range of contributions to Intel software products such as Intel® MKL, Intel® IPP, Intel compilers, and others. Among his recently completed projects was the Intel® Data Analytics Acceleration Library. Sergey received a master's degree in Mathematics from the State University of Nizhny Novgorod with specializations in number theory, random number generation, and its application in financial math. He was previously a staff member of the International Center of Studies in Financial Institutions at the State University of Nizhny Novgorod.

Tom Radcliffe, VP Engineering, ActiveState Software
Tom Radcliffe is VP Engineering for ActiveState, and has over 20 years experience in software development, data science, machine learning, and management in both academia and industry.

He is a professional engineer (PEO and APEGBC) and holds a PhD in physics from Queen's University at Kingston. Tom brings a passion for quantitative, data-driven processes to ActiveState. He is deeply committed to the ideas of Bayesian probability theory, and assigns a high Bayesian plausibility to the idea that putting the best software tools in the hands of the most creative and capable people will make the world a better place.