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.
Join Sergey Maidanov, Software Engineering Manager at Intel and Tom Radcliffe, VP Engineering at ActiveState as they 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