Online or onsite, instructor-led live RAPIDS training courses demonstrate through interactive hands-on practice how to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms, such as XGBoost, cuML, etc.
RAPIDS training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live RAPIDS training can be carried out locally on customer premises in Surrey or in NobleProg corporate training centers in Surrey.
NobleProg -- Your Local Training Provider
Surrey - King Cross
7404 King George Blvd, Surrey, Canada, V3W 1N6
In one of Canada's fastest growing major cities, our King's Cross business centre is a practical place to do business in Surr...
In one of Canada's fastest growing major cities, our King's Cross business centre is a practical place to do business in Surrey. As well as on-site businesses from banking, insurance and beyond, regular bus links and the King George Boulevard offer fast, convenient connections to the city centre. Newton Exchange the main bus hub connecting to south Surrey the lower main land is but a block away.
King's Cross is a popular mixed-use community site, home to coffee shops, retailers and fast food - ideal for breaks or informal meetings. And with heavy footfall and visibility from the highway, it combines the exposure you'd expect from a city centre with the convenience of an out of town location.
This instructor-led, live training in Surrey (online or onsite) is aimed at data scientists and developers who wish to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms, such as XGBoost, cuML, etc.
By the end of this training, participants will be able to:
Set up the necessary development environment to build data models with NVIDIA RAPIDS.
Understand the features, components, and advantages of RAPIDS.
Leverage GPUs to accelerate end-to-end data and analytics pipelines.
Implement GPU-accelerated data preparation and ETL with cuDF and Apache Arrow.
Learn how to perform machine learning tasks with XGBoost and cuML algorithms.
Build data visualizations and execute graph analysis with cuXfilter and cuGraph.
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