Fast and scalable machine learning in Python for data scientists

Por David A Liu,

Publicado:08/28/2018   Última actualización:08/28/2018

The Python language has become integral to almost every data scientist’s workflow. There are countless easy-to-use Python data science packages, ranging from data analysis and visualization, to machine learning, to an interactive development environment that enables rapid iteration over data and models. Python is used to do facial recognitionsentiment analysisfraud detectionbrain tumor classification, and much more.

Intel®’s accelerated Python packages enable data scientists to take advantage of the productivity of Python, while taking advantage of the ever-increasing performance of modern hardware. Inte®’s optimized implementation of scikit-learn (leveraging Intel® Data Analytics Acceleration Library), as well as Intel®’s optimized implementations of Tensorflow and Caffe (leveraging Intel® MKL-DNN), to achieve highly efficient data layout, cache blocking, multi-threading, and vectorization. Intel®’s optimized implementations of numpy and scipy provide drop-in performance enhancement to the vast ecosystem of statistics, mathematical optimizations, and many other data-centric computations already built on top of numpy and scipy. Furthermore, Intel® now provides daal4py, which combines the one-liner API simplicity akin to scikit-learn, with automatic scaling over multiple compute nodes. These all help data scientists deliver better predictions faster, and enable analysis of larger data sets with the same compute and memory resources without choking.

Get started now. Download the Intel® Distribution for Python or select pip/conda packages.

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