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 Rotterdam or in NobleProg corporate training centers in Rotterdam.
NobleProg -- Your Local Training Provider
Rotterdam
Novotel Rotterdam Brainpark, K.P. van der Mandelelaan 150, Rotterdam, Netherlands, 3062 MB
The meeting rooms in Brainpark in Rotterdam are located at one of the most prestigious business addresses of the city, on the...
The meeting rooms in Brainpark in Rotterdam are located at one of the most prestigious business addresses of the city, on the most famous business park in Rotterdam. Among the locally established companies are international and smaller specialized companies in the service sector, mainly financial, consultancy and cargo-related services, which shows the importance of Rotterdam as an international deep-water sea and river port, the gateway to European markets for a wide selection industrial and consumer products.
This instructor-led, live training in Rotterdam (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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