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 Örebro or in NobleProg corporate training centers in Örebro.
NobleProg -- Your Local Training Provider
Örebro, City Center
NobleProg classroom in Clarion Hotel Örebro, Kungsgatan 14, Örebro, sweden, 702 11
Located in central Örebro in the midst of the shopping and business district. You can find popular tourist destinations, suc...
Located in central Örebro in the midst of the shopping and business district. You can find popular tourist destinations, such as Orebro Castle, Wadköping village and Gustavsvik water park near the hotel. Train to Örebro central station or
Örebro Södra station. Remote bus to Örebro central station. Local bus to bus stop Våghustorget. We recommend Taxi Courier or Sweden Taxi. 15-minute drive to Örebro Läns airport.
This instructor-led, live training in Örebro (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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