Accelerate GIS data processing with RAPIDS

  • RAPIDS API scales to multiple GPUs, so the combined result of multi-node and multi-GPU provides incredible speed, without changing the code
  • Data is not limited to the size of your GPU because you can scale both across nodes and GPUs within those nodes
  • Pre-configured environment with the latest RAPIDS and GPU-related packages so you can start developing right away
  • No delays in shipping to production: more than just a playground for experimentation. After development and testing, you can deploy and serve your model right away by committing them to your favourite git repository
  1. Make the platform easy as pie by adding relevant frameworks as they emerge in the industry (hint: we’ve already implemented Dask-SQL, stay tuned for new upgrades)
  2. Solid UI/UX, achieved by adding more automation, alerting, and other capabilities so that a data science team can go from beginning to end on their own

--

--

--

An end-to-end data science platform

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Coronavirus — StopTheCurve, not FlattenTheCurve

World on fire

Huber and Ridge Regressions in Python: Dealing with Outliers

An application of Dynamic Time Warping (DTW): Matching events between signals

Python Vs R programming For Data Science: Here’s Why I prefer Python

Check Yo’ Data Before You Wreck Yo’ Results

The ABCs of Digital Transformation Terminology for Oil and Gas

What Data Analysts Should Know About Product Analytics

Why you should summarize your data with the geometric mean

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Shakudo

Shakudo

An end-to-end data science platform

More from Medium

Geo Clustering : An Application of DBSCAN

DeepGlobe Road Extraction — Challenge

Kubeflow Pipelines for Earth Observation

Spatial sampling and resampling for Predictive Mapping with Machine Learning: A tutorial in R