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 mateusz dymczyk


H2O.ai Joins NVIDIA AI Conference to Support Growing Demand for AI and Machine Learning in Australia

#artificialintelligence

H2O.ai, the open source leader in AI, today announced an expanded presence in Australia due to heightened demand from customers for automatic machine learning and data science solutions in the region. As part of its efforts to democratize AI globally, H2O.ai will sponsor and attend the NVIDIA AI Conference in Sydney next week, as well as host a meetup to engage with data science professionals about its award-winning machine learning platforms and provide Australian businesses with the power of scalable AI. H2O.ai's expanded efforts in Australia come at a time of unprecedented demand for its products in the region. Since its launch in late 2017, the company's automatic machine learning platform Driverless AI has been deployed by customers, including Stanley Black and Decker, Armada Health, Deserve, G5 and more. NVIDIA AI Conference is a premier event on artificial intelligence and deep learning, and showcases the latest breakthroughs from universities, startups and major enterprises in a wide range of fields such as smart cities, autonomous machines, virtual reality and more.


MapD & H20.ai: GPU-powered Visualization and Machine Learning

@machinelearnbot

A revolution is taking place in the GPU software stack in the fields of analytics, machine learning and deep learning, driven by NVIDIA's hardware innovation, that provides 100x more processing cores and 20x greater memory bandwidth than CPUs. However, systems and platforms are unable to harness these disruptive performance gains because they remain isolated from each other. The GPU Open Analytics Initiative (GOAI) and its first project, the GPU Data Frame (GDF) was created to allow seamless passing of data between processes. At this meetup, we'll explain how we have implemented an end-to-end machine learning powered by GOAI. We will show how GDFs break down the silos to enable interactive data exploration, model training, and model scoring, that is lightning-fast by virtue of avoiding any serialization overhead.