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 trainings in Csongrád can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Szeged
NobleProg Szeged, Maros utca 1., Szeged, hungary, 6721
The NobleProg conference rooms are located in the city center, right next to the garden of the beautiful castle of Szeged and...
The NobleProg conference rooms are located in the city center, right next to the garden of the beautiful castle of Szeged and close to the Tisza river. These conference rooms can be hired as lecture, seminar, teaching or meeting rooms. With natural lighting from two large windows, a variety of artificial lighting options and great technical equipments, such as projector, computers, full sound system with speakers, and internet, this meeting room has great possibilities for successful presentations or trainings. Our professional staff is taking care of the preparation and implementation of the high quality conferences.
This instructor-led, live training in Csongrád (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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