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. Pécs onsite live RAPIDS trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Pécs, Belváros
NobleProg Pécs, Király u. 1, Pécs, hungary, 7626
Our venue in Pécs is located in the city center, in a quiet neighbourhood surrounded by cozy cafés and local restaurants. T...
Our venue in Pécs is located in the city center, in a quiet neighbourhood surrounded by cozy cafés and local restaurants. The famous Zsolnay Cultural Quarter and the Kodály center are also only a few minutes walk away. This location is not only a convenient choice to touist but also people arriving here for business purposes.
The conference rooms are equipped with modern technology, they can be sectioned, and they can accomodate a maximum of 200 people.
This instructor-led, live training in Pécs (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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