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. Bordeaux onsite live RAPIDS trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Bordeaux, Mériadeck
Nomade Bordeaux, 39 Rue du Château d'Eau, Bordeaux, france, 33000
Located in the heart of Bordeaux, in the Mériadeck quarter, fully equipped and air-conditioned, NOMADE allows mobile prof...
Located in the heart of Bordeaux, in the Mériadeck quarter, fully equipped and air-conditioned, NOMADE allows mobile professionals to settle down to work and share with other coworkers in a friendly atmosphere.
It offers 23 workstations in open space, a shared meeting room and 5 meeting rooms for rent in Bordeaux center.
The entire space can also be rented in the evening for your events.
Access: TRAM A MERIADECK station, TRAM B GAMBETTA station
This instructor-led, live training in Bordeaux (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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