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Data scientists need the results of their calculations as fast as possible. Why wait when Intel’s optimized Python packages deliver quick repeatable results compared to standard Python packages? Intel offers optimized Scikit-learn, Numpy, and SciPy to help data scientists get rapid results on their Intel® hardware.
Achieve real performance benefits.
Download the free Intel® Distribution for Python* that includes everything you need for blazing-fast computing, analytics, machine learning, and more. And look into daal4py with its high-speed, open-source machine-learning algorithms with single-line APIs that support streaming data, data distributed over multiple nodes, and efficient model export.
Why is Intel Distribution so fast? Here are three reasons:
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The core computing packages, Numpy*, SciPy, and scikit-learn*, are accelerated under the hood with powerful, multithreaded native performance libraries such as Intel® Math Kernel Library, Intel® Data Analytics Acceleration Library, to deliver native, code-like performance results to Python.
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2.
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daal4py can be used directly or through Scikit-learn* to access optimized algorithms such as K-means clustering, Random Forest*, logistic regression, k-nearest neighbors (KNN), support vector machines (SVM), and many more.
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3.
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It is possible to accelerate your existing Scikit-learn codes with zero changes with Scikit-learn calculations that are repeatable and continue to behave in the manner that you expect.
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All this is available out of the box with the free Intel Distribution for Python. Intel Python is available via Conda, pip, or Docker, and our all-in-one installer.
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