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 Coventry or in NobleProg corporate training centers in Coventry.
NobleProg -- Your Local Training Provider
Coventry
Warwick Road, Coventry, united kingdom, CV1 2DY
The business centre is located within the sought after professional district of the city centre of Coventry. The workspace is...
The business centre is located within the sought after professional district of the city centre of Coventry. The workspace is within a five minute walk of Coventry train station, public transport routes, car parking facilities (we also have onsite car parking), banks, estate agents and city council offices.
Based in the midlands the centre is has convenient access to both main road, motorway and rail networks with only a short commute to Birmingham International and Coventry airports.
This instructor-led, live training in Coventry (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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