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 Regina can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Regina - Royal Bank Building
2002 11th Ave, Regina, Canada, S4P 0J3
Grow your business in one of Regina’s most prominent finance hubs. Build your base in the prestigious Royal Bank Building, ...
Grow your business in one of Regina’s most prominent finance hubs. Build your base in the prestigious Royal Bank Building, home to several leading financial and professional services companies.
Impress your clients and colleagues with the building’s commanding city views, before getting to work in stylish surroundings with plenty of natural light. And travel easily for work or pleasure, with the nearby Regina International Airport offering regular flights across Canada and beyond.
This instructor-led, live training in Regina (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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