- Join us for this exclusive webinar on accelerating data science workloads with GPUs, organised by Analytics India Magazine in association with NVIDIA & Micropoint Computers Pvt Ltd.
Data Science is transformational and is key to unlocking value from data. It is being used in every industry for a large number of use cases, from forecasting to fraud detection, from recommender systems to drug discovery, from churn reduction to risk evaluation and many more.
Traditionally one of the challenges for data scientists has been the slow processing power of CPUs. With the end of Moore’s Law, CPUs aren’t getting significantly faster year after year. The combination of large data sets and slow processing power of CPUs makes the data scientists sit through long periods of processing time leading to delays in machine learning models.
NVIDIA delivers key benefits to organisations using Data Science. With NVIDIA GPUs, Data Scientists achieve two key benefits – faster time to insight & more iterations on the data sets – leading to better model accuracy.
In addition, NVIDIA launched CUDA-X AI SDK for GPU-accelerated analytics and data science in 2019, intending to provide an end-to-end platform for the acceleration of data science. In addition, it addresses one of the biggest challenges data scientists face today — accessing process-intensive operations.
To know more about how NVIDIA GPUs and SDKs can help you accelerate your data science projects, join us for the webinar on “Accelerating Data Science Workloads with GPUs” organised by Analytics India magazine in association with NVIDIA & Micropoint Computers Pvt Ltd on 6th October 2021.
Helmed by Saurav Agarwal, Sr. Enterprise Architect – Big Data, Advanced Analytics & ML, at NVIDIA, the session will cover how data science pipelines work in the industry and how they can be supercharged with open-source frameworks to speed up them.
The webinar will cover —
- Understanding the workings of a data science pipeline & how the performance can be enhanced with open source frameworks.
- Get familiarised with data science architecture and workflows in the industry
- Get hands-on examples of speeding up these workflows using open source libraries.
Who should attend?
- Data science, data engineering, analytics & AI enthusiasts
- Data engineering professionals & aspirants
- Aspiring data scientists
- Working professionals interested in the analytics domain
- Data science & analytics professionals looking to pivot
- Students from engineering/technical background
- Graduates of Maths, Science, Economics or Statistics
Saurav Agarwal – Sr. Enterprise Architect – Big Data, Advanced Analytics & ML, NVIDIA
Saurav has around ten years of data industry experience implementing AI/data science/analytics solutions on big data platforms, including large-scale data lake systems. He is an experienced senior architect and seasoned data engineer with experience building distributed real-time data science pipelines. Along with having hands-on architecture and implementation experience in enterprise data landscapes, including Hadoop and Spark ecosystems, Saurav has been part of multiple large-scale projects covering end-to-end data landscape solutions for automotive, supply chain, healthcare, banks, fintech, and more. His top projects include streaming predictive alerts of heart ailments for a primary healthcare provider and building a petabyte-scale data lake for a large fintech firm and its various partner consumers.