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. Marseille onsite live RAPIDS trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Marseille, Prado Quarter
Mutliburo Marseille, 565 Avenue du Prado, Marseille, france, 13008
At the heart of the prestigious business quarter of Avenue Prado, surrounded by nature and offering a beautiful view on the s...
At the heart of the prestigious business quarter of Avenue Prado, surrounded by nature and offering a beautiful view on the see.
The business center offers companies a wide range of services and technical means with advanced technology, thus encouraging the development of the activity of its customers.
Plane: 40 minutes away fromInternational Airport Marseille-Provence.
Bus: 19 et 83 lines. "La Plage" Station.
Tram: Line 2. Stop " Rond-Point du Prado"
This instructor-led, live training in Marseille (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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