In this episode of the O’Reilly Podcast, I speak with Amit Vij, CEO and co-founder of Kinetica, a company that has developed an analytics database that uses graphics processing units (GPUs). We talk about how organizations are using GPU-accelerated databases to converge artificial intelligence (AI) and business intelligence (BI) on a single platform.
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The benefits of converging AI and BI in a single system: “You are orders of magnitude faster, and you have the ability to operate on real-time data, as opposed to operating on yesterday’s data,” Vij says.
The processing speed of GPUs: “The GPU really leverages parallel processing so you can maximize your throughput and take advantage of the advancements in hardware that have come about,” he says.
How GPU databases break down the walls between the data science and business domains: “Nowadays machine learning scientists and mathematicians can, in just three lines through SQL, execute their algorithms directly on data sets that are billions of objects,” Vij says.
How GPU databases integrate with machine learning tools such as TensorFlow, and how GPU applications can use the cloud