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Introduction Geospatial data is everywhere. Mapping directions, tracking a package, and reading a weather report are all examples of how we use geospatial data in our daily lives. In recent years, the nature of geospatial data has changed. Data is generated from different sources, such as technologies like IoT, drones, and autonomous vehicles, as well…
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 enable data scientists and data engineers to build applications with artificial intelligence, all while leveraging the…
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 at the core of the Python GPU dataframe (pyGDF). Our new open source integration with…
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…
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…
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