IBM has recently announced that it plans to incorporate the new RAPIDS open source software into its enterprise-grade data science platform for on-premises, hybrid, and multicloud environments. 'IBM has a long collaboration with NVIDIA that has shown demonstrable performance increases leveraging IBM technology, like the IBM POWER9 processor, in combination with NVIDIA GPUs,' said Bob Picciano, senior vice president of IBM Cognitive Systems. 'We look to continue to aggressively push the performance boundaries of AI for our clients as we bring RAPIDS into the IBM portfolio.' RAPIDS will help bring GPU acceleration capabilities to IBM offerings that take advantage of open source machine learning software including Apache Arrow, Pandas and scikit-learn. Immediate, wide ecosystem support for RAPIDS comes from key open-source contributors including Anaconda, BlazingDB, Graphistry, NERSC, PyData, INRIA, and Ursa Labs.
Nvidia is no stranger to data crunching applications of its GPU architecture. It's been dominating the AI deep learning development space for years and sat rather comfortably in the scientific computing sphere too. But now it's looking to take on the field of machine learning, a market that accounts for over half of all data projects being undertaken in the world right now. To do so, it's launched dedicated machine learning hardware in the form of the DGX-2 and new open-source platform, Rapids. Designed to work together as a complete end-to-end solution, Nvidia's GPU-powered machine learning platform is set to completely change how institutions and businesses crunch and understand their data.
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."
No matter the industry, data science has become a universal toolkit for businesses. Data analytics and machine learning give organizations insights and answers that shape their day-to-day actions and future plans. Being data-driven has become essential to lead any industry. While the world's data doubles each year, CPU computing has hit a brick wall with the end of Moore's law. For this reason, scientific computing and deep learning have turned to NVIDIA GPU acceleration.
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.