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 Linz or in NobleProg corporate training centers in Linz.
NobleProg -- Your Local Training Provider
NobleProg Linz
Hafenstraße 47-51, Linz, Austria, 4020
Overview
Our training facilities are located at Hafenstraße 47-51 in Linz. Our spacious ...
Overview
Our training facilities are located at Hafenstraße 47-51 in Linz. Our spacious training rooms are located in the old town, near the Danube, and offer optimal training conditions for your needs.
Directions
The NobleProg training facilities are conveniently located near the A7 motorway.
Parking spaces
There are parking spaces in the surrounding streets around our training rooms.
Local infrastructure
There are numerous restaurants in the area of the old town and hotels are also within walking distance.
This instructor-led, live training in Linz (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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