I'm going to talk about how Intel makes your Scikit-Learn

faster with the Intel Math Kernel Library and Intel Data

Analytics Acceleration Library.

Stay here to learn more.

Also, follow the links below for more information.

[MUSIC PLAYING]

With Intel Distribution for Python,

we provide performance optimized Python packages.

You know our latest release, Scikit-Learn

got another performance boost by our highly optimized

compute engine, Intel DAAL.

Previous versions of Intel Scikit-Learn

already show decent speed-ups over standard versions,

such as packages delivered by [INAUDIBLE] Pythons.

Scikit-Learn uses NumPy and ScyPi for its compute kernels

and by accelerating NumPy, we were

able to achieve significant performance

gains in Scikit-Learn without even touching its code.

Our version of NumPy uses Intel MKL internally

so it gets best in class performance.

The speed-ups achievable with accelerated NumPy

range from a few percent to factors up to eight.

In our latest release, we further

optimized selected kernels from Scikit-Learn

by using Intel DAAL, which is also a specialized performance

library.

Intel DAAL provides highly optimized building blocks

needed to build your analytics pipeline and machine learning

algorithms.

It not only covers the core functionality

like analysis, decision making, and modeling, but also IO,

and data manipulation.

The algorithms we currently support now

show extreme speed-ups over the previous version.

The performance is now close to native DAAL performance, which

can be considered as best in class.

Scikit-Learn is a mature Python package

with hundreds of algorithms with different configuration

parameters each.

DAAL has a different set of algorithms

and sometimes implementations use slightly different variants

of the algorithm.

To make sure the use of optimized DAAL

gives valid results, we make sure that only

mathematically equivalent implementations

are used from DAAL.

Configurations without an equivalent in DAAL

will fall back to Scikit-Learn's only limitation.

It.

Additionally, we allow easy, on the fly enabling and disabling

these DAAL optimizations.

This is done by simply calling enable or disable,

and can be applied to each algorithm individually.

Last, but not least, I'd like to mention that DAAL also

comes with its own Python API, which lets you utilize

its full power directly.

It operates with other Python packages through NumPy arrays.

So you can easily combine it with anything that

also works with NumPy arrays.

Of course, Scikit-Learn is one of these.

Thanks for watching.

To learn more, or access anything

discussed in this video, follow the links below.