Combined technology of Kinetica’s Apache Arrow integration code with RAPIDS software will streamline data science and data engineering processes
Kinetica is a US based company that develops a distributed, in-memory database management system using graphics processing units. The company has announced about making open source code available to RAPIDS open source software for integration. RAPIDS is a new machine learning training stack introduced by NVIDIA.
The union of Kinetica with RAPIDS will help data scientists and data engineers to work again on artificial intelligence by xploring, training, visualizing, and integrating machine intelligence into smart analytical applications. This will help in accelerating the computing power of GPU.
Today, AI is the main strategy for the enterprises, so it is very important to streamline data science and data engineering processes. RAPIDS boost the power of NVIDIA GPUs and this minimizes AI model training time from days to minutes.
Kinetica combines a GPU database, real-time analytical techniques (location intelligence, time series, text search), and the ability to run analytics and pre-trained machine learning models in-database. Combinatin of RAPIDS and Kinetica provide enterprises with a concrete way to realize the end-to-end impact of AI, whether it be driving cars, stocking warehouses, or making personalized recommendations.
Kinetica and RAPIDS runs seamlessly on the GPU and communicate without copying data to the CPU, because Kinetica integration is based on the Apache Arrow project.
Very soon Kinetica will provide Apache Arrow integration code available to developers through GitHub. RAPIDS and Kinetica can be downloaded from the NVIDIA GPU Cloud.
“We’re excited to support Apache Arrow, a core component of accelerated analytics on the GPU. Our latest open source capabilities enable us to seamlessly integrate with RAPIDS across the GPU-powered data ecosystem,” said Nima Negahban, co-founder and CTO of Kinetica. “While NVIDIA drives model development and training, Kinetica drives operationalization and deployment of those models in-database, so enterprises gain maximum insight from their data.”
“Companies are increasingly data-driven, but speed is of the essence to use this data,” said Jeffrey Tseng, head of product for AI Infrastructure at NVIDIA. “With RAPIDS and Kinetica, enterprises can leverage the power of the GPU and advanced analytics across the model development toolchain and dramatically simplify and speed up the data science pipeline.”
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