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Nvidia launches Rapids to help bring GPU acceleration to data analytics


Nvidia, together with partners like IBM, HPE, Oracle, Databricks and others, is launching a new open-source platform for data science and machine learning today. Rapids, as the company is calling it, is all about making it easier for large businesses to use the power of GPUs to quickly analyze massive amounts of data and then use that to build machine learning models. "Businesses are increasingly data-driven," Nvidia's VP of Accelerated Computing Ian Buck told me. "They sense the market and the environment and the behavior and operations of their business through the data they've collected. We've just come through a decade of big data and the output of that data is using analytics and AI. But most it is still using traditional machine learning to recognize complex patterns, detect changes and make predictions that directly impact their bottom line."

Nvidia and IBM team up on open source machine learning Internet of Business


NEWSBYTE IBM has announced a new partnership with AI and GPU hardware giant Nvidia, bringing the latter's Rapids open source data science toolkit into IBM's data science platform for on-premise, hybrid, and multi-cloud environments. Rapids will bring GPU acceleration capabilities to IBM's offerings, taking advantage of an ecosystem that includes the Web-based big data platform, Anaconda (an open source distribution of the Python and R programming languages for data science and machine learning), Apache Arrow, Pandas, and scikit-learn. Rapids is also supported by open-source contributors, including BlazingDB, Graphistry, NERSC, PyData, INRIA, and Ursa Labs. IBM's Power 9 with PowerAI environment will be among those benefiting from the tie-up. It will use Rapids to expand the options available to data scientists with new open-source machine learning and analytics libraries.

NVIDIA Brings The Power Of GPU To Data Processing Pipelines


NVIDIA has launched an open source project called Real-time Acceleration Platform for Integrated Data Science (RAPIDS) that aims to deliver end-to-end data science infrastructure based on GPUs. GPU-backed machines play an essential role in generating machine learning models. Data scientists run training jobs that are computationally intensive on GPUs. Massive datasets that are converted into complex matrices of numbers are used as an input for machine learning and deep learning models. During the training process, these matrices are added, multiplied and subtracted from other complex matrices.

NVIDIA's RAPIDS Brings GPU Power to Predictive Data Analytics - Avionics


NVIDIA VP for Accelerated Computing Ian Buck unveiled the graphics processing unit (GPU) provider's new open-source platform, RAPIDS, which promises major potential for accelerating the ability for data scientists to incorporate neural networks and machine learning into data analytics platforms. Buck unveiled RAPIDS as part of an hour-long opening keynote during NVIDIA's GPU Technology Conference (GTC) at the Ronald Reagan Building in Washington D.C. this week. NVIDIA has been hosting GTCs throughout 2018 to explain how artificial intelligence, machine learning and other embedded computing processing concepts can be applied in innovative new ways to new industries. Washington D.C. comes after GTCs in Europe, Israel and Japan, with the final one of the year scheduled for China next month. RAPIDS is NVIDIA's new open-source software that serves as a GPU-acceleration platform to give companies ability to analyze massive amounts of data and make accurate business predictions at unprecedented speed.

2019 AI Trend To Watch: Open Source and RAPIDS


Practical applications of Artificial Intelligence (AI) technologies gained momentum in 2018. For example, machine learning algorithms were used across various industries to support use cases as diverse as predicting credit card fraud, minimizing flight delays and offering tailored content recommendations. Natural language processing algorithms were used to help companies understand customer sentiment. Meanwhile, vision-based models improved quality control in manufacturing and helped automotive companies push further into autonomous driving. GPUs provided a hardware foundation for training a majority of the large-scale machine learning models.