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Kinetica Sparse Data Tutorial

By Chad Juliano | September 27, 2018

Introduction This tutorial describes the application of Singular Value Decomposition or SVD to the analysis of sparse data for the purposes of producing recommendations, clustering, and visualization on the Kinetica platform. Sparse data is common in industry and especially in retail. It often results when a large set of customers make a small number of…

Bring Your Deep Learning Model to Kinetica

By Zhe Wu | August 31, 2018

How can we avoid the data science black hole of complexity, unpredictability, and disastrous failures and actually make it work for our organizations? According to this recent Eckerson article the key is operationalizing data science: We, as a field, and I mean academics, scientists, product developers, data scientists, consultants … everybody … need to redirect…

Accelerating Connected Car Development with Automotive Grade Linux

By Ken Wattana | August 29, 2018
Kinetica Joins AGL

I’m excited to share that Kinetica’s now a bronze member of Automotive Grade Linux (AGL)! AGL is a collaborative open source project that’s bringing together automakers, suppliers, and technology providers to accelerate development and adoption of connected vehicle services. Members include Toyota, Honda, Mazda, Mercedes, Mitsubishi, Suzuki and many other key automotive players. The goal…

Introducing Kickbox.js, a Code Acceleration Library for Kinetica and Mapbox

By Matt Brown | August 29, 2018

The explosion of mobile phones, connected cars, and the Internet of Things has enabled us to capture, store, and share geospatial information at unprecedented scale. Location intelligence is taking over the world, powering innovative services like autonomous vehicles, location-based offers, and real-time logistics. Understanding the context of how people, devices, and things interact with the…

Moving AI From Science Experiment To The Mainstream

By Dipti Borkar | August 23, 2018

For AI to become mainstream, it will need to move beyond small scale experiments run by data scientists ad hoc. The complexity of technologies used for data-driven machine and deep learning means that data scientists spend less time developing algorithms and more time automating and operationalizing the process. Business analysts, on the other hand, find…

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