5 min
A GPU is one of the most important components of modern-day artificial intelligence and deep learning architecture. Enterprises and developers are constantly on the lookout for tools that help them build and manage end-to-end data science and analytics pipelines seamlessly. RAPIDS is one such a tool incubated by NVIDIA® based on the company’s expert experience in hardware and data science.
RAPIDS uses NVIDIA CUDA® primitives for low-level compute optimisation, and lets developers use GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
RAPIDS’s also helps with data preparation tasks for data science pipelines. With support for multi-node and multi-GPU deployments, RAPIDS is fast becoming a favourite among deep learning and data science developers.
With this in mind, NVIDIA and Analytics India Magazine are arranging a day-long workshop which will help developers and data scientists to fully leverage and improve their data science pipeline using RAPIDS.
To apply for the workshop, click here.
RAPIDS also includes projects like cuDF, a powerful data manipulation library; cuML, a collection of machine learning libraries; and cuGraph, an efficient and accelerated graph analytics library. The projects are well maintained with highly-efficient communities.
Analytics India Magazine will be teaming up with NVIDIA to bring to engineers and data scientists a workshop focused on techniques such as RAPIDS, Data ETL Pipeline and algorithms. Attendees will be able to learn how to use RAPIDS and have hands-on experience with data ETL pipelines.
To apply for the workshop, click here.
Titled ‘NVIDIA RAPIDS GPU-Accelerated Data Analytics & Machine Learning Workshop’ the workshop will offer industry insights on how to make your data science pipelines better. The workshop will include a keynote conducted by:
Sundara Ramalingam Nagalingam - Head Deep Learning Practice, NVIDIA
Mitra Rath - Senior Solution Architect, NVIDIA
The workshop will take the participants through the following topics:
- Current Challenges in Machine learning and data science
- RAPIDS Technical Overview and Software Architecture
- Hand-on Session Covering RAPIDS end-to-end data science workflow
- Data Engineers and Data Scientists looking to supercharge their training and inference workflows.
- Data Science managers looking for an upgrade to existing infrastructure.
- AI/ML enthusiasts with experience in basic concepts of ML, data science, workflows and have worked with Python, Scikit-learn, or Pandas.
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