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 Maryland or in NobleProg corporate training centers in Maryland.
NobleProg -- Your Local Training Provider
MD, Baltimore - Legg Mason Tower
100 International Drive 23rd Floor, Baltimore, united states, 21202
A state-of-the-art, 24-story glass skyscraper that sits on the edge of Baltimore's Inner Harbor is the signature home ...
A state-of-the-art, 24-story glass skyscraper that sits on the edge of Baltimore's Inner Harbor is the signature home of the Legg Mason office. It's located on the 23rd floor of this class-A office development, which is designed by world-renowned architects and boasts panoramic views. The office space benefits from the tower's ‘green' LEED credentials, proximity to Interstate 83 and excellent onsite amenities. Inner Harbor is the chief commercial and tourist destination in Baltimore and part of the Downtown area - the base for many key businesses.
This instructor-led, live training in Maryland (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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