Get an exclusive inside look from some of the experts behind the curtain at Intel and ActiveState on how data scientists can speed up their algorithms.
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 will look at:
- Role of productivity and performance for numerical computing and machine learning
- Speeding up Python when writing the algorithms:
- Algorithm choice
- Understanding efficient Python package usage
- Requirements for efficient use of hardware and why stock Python is slow
- Intel MKL and Python package mapping
- MKL-accelerated Numpy and Scipy.
- How other Intel technologies help to accelerate and scale Python performance
- How you can get all these optimizations
Space is limited, reserve your spot today:
Sergey Maidanov, Software Engineering Manager, Intel
Tom Radcliffe, VP Engineering, ActiveState
Hope to see you there!
-The ActiveState Team