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 Burnaby or in NobleProg corporate training centers in Burnaby.
NobleProg -- Your Local Training Provider
Burnaby - Metrotown
Suite 2600, 4720 Kingsway, Burnaby, Canada, V5H 4N2
The Burnaby Metrotown Centre is a 30-storey office building which is centrally located by Metrotown Sky train station and con...
The Burnaby Metrotown Centre is a 30-storey office building which is centrally located by Metrotown Sky train station and connected to the Metropolis shopping mall in Burnaby.
Burnaby Centre
Suite 200, 4170 Still Creek Drive , Burnaby , Canada, V5C 6C6
The Burnaby Centre is in a premier business park in North Burnaby. It is located close to Highway 1 and within walking distan...
The Burnaby Centre is in a premier business park in North Burnaby. It is located close to Highway 1 and within walking distance of Brentwood Town Centre and Gilmore SkyTrain Station.
This instructor-led, live training in Burnaby (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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