Your Path to Deeper Insight
Machine learning is becoming faster and more accessible. Our customers are using massive data sets to build smarter cities, power intelligent cars, and deliver personalized medicine…and that is just the beginning.
Data scientists, developers, and researchers are using machine learning to gain insights previously out of reach. Programs that learn from experience are helping them discover how the human genome works, understand consumer behavior to a degree never before possible, and build systems for purchase recommendations, image recognition, and fraud prevention, among other uses.
Now you can scale your machine learning and deep learning applications quickly – and gain insights more efficiently – with your existing hardware infrastructure. Popular open frameworks newly optimized for Intel, together with our advanced math libraries, make Intel® Architecture-based platforms a smart choice for these projects.
Big speed jumps like those Intel is seeing in performance on specific algorithms are rarely possible in the traditional high-performance computing world, where problems are well-defined and optimization work has already been happening for many years. Machine learning algorithms still have room for improvement.
Andrey Nikolaev, a software architect on the team in Russia, says that at given times, it appears that he and his colleagues have optimized an algorithm to the maximum extent possible.
“Then tomorrow we will understand—or someone will come to us and show us—how we can make it faster,” he says. “Optimization is something you can do forever.”
The popular open-source development framework for image recognition is now optimized for Intel® architecture.
Intel’s open-source Trusted Analytics Platform provides prebuilt machine learning functions, making it easier to build public and private cloud analytics apps.
Tools, techniques, and frameworks that boost machine learning performance on Intel® architecture.
Highly optimized library that helps speed big data analytics by providing algorithmic building blocks for all data analysis stages and for offline, streaming, and distributed analytics usages.