Kinetica in Motion
Converging Data Science and Data Engineering with Our Open Source Integration for RAPIDS
Recently, NVIDIA announced RAPIDS, an open source data science library that enables data scientists to accelerate model training and development by harnessing the power of the GPU. Today, we’re pleased to share our open source integration with RAPIDS to …
Read More
Working with RAPIDS Using Kinetica’s pyGDF Open Source API
With the rise of GPU computing, streamlining the processing of data on GPUs has become critical to increase the speed and efficiency of machine learning. The RAPIDS open source data library is based on the Apache Arrow specification that’s also …
Read MoreGetting to Know the Automotive Grade Linux Community at AGL Dresden
Last week Kinetica took a trip to our first Automotive Grade Linux (AGL) All Members Meeting in Dresden! This follows our announcement in August that we’re now members of the Linux Foundation and AGL. The conference was a great opportunity …
Read MoreGPUs in Germany, a Recap from NVIDIA GTC Europe
Auf Wiedersehen Germany! Last week we wrapped up a highly successful GPU Technology Conference (GTC) Europe in Munich! GTC is NVIDIA’s international conference series, bringing together the top minds in deep learning, analytics, and of course GPUs for sessions, workshops, …
Read MoreBringing Data Science and Data Engineering Together with RAPIDS Open-Source Software From NVIDIA and Kinetica
In a significant step forward in enabling the adoption of AI at scale, NVIDIA today announced their latest GPU-powered innovation for data science, the RAPIDS suite of open-source libraries. RAPIDS will allow data scientists to move workflows for model training …
Read More
Kinetica Sparse Data Tutorial
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 …
Read MoreBring Your Deep Learning Model to Kinetica
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 …
Read MoreAccelerating Connected Car Development with Automotive Grade Linux
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…
Read More
Introducing Kickbox.js, a Code Acceleration Library for Kinetica and Mapbox
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, …
Read More
Moving AI From Science Experiment To The Mainstream
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 …
Read More