Four Ways Accelerated Computing is Changing Data Science

AI and machine learning continue to help businesses obtain and process enormous amounts of data. But without a way to process the massive volumes of information, there’s little benefit to be gained from the data. 


Unfortunately, end-to-end data science practices are complicated to build. And even with the best available data scientists, there are still many roadblocks to making data useful for businesses. 


There may be a solution to this problem with accelerated computing. Here are 4 key advantages to accelerated computing from a recent article by Inside Big Data.

1. Sharing Data and Solving Problems Faster

Centralized, shared infrastructures at supercomputing scales are being used by companies to speed up the ROI of AI.


In other instances, organizations will use a hybrid approach, combining data center infrastructure and the cloud. 


Regardless of the methods used, they’re meant to be catalysts for scaling and grooming data science workers. This way, complex AI problems will be solved faster, and best practices can be shared quickly.

2. The RAPIDs Solution

All leading cloud service providers have been working with AI computing leader NVIDIA. As a result, complex data sets are being transformed and analyzed while machine learning automates the analysis. 


Accelerated computing platforms are involved in the bulk of these collaborations. They’re combining hardware and software to boost the speed of data analytics.  


At the center of these projects is a suite of open-source software libraries and APIs called RAPIDS. This tool runs end-to-end data science and analytics pipelines strictly on NVIDIA Graphics Processing Units (GPUs). 


Currently, Walmart is an active platform contributor and is internally using RAPIDS


The retail superpower uses accelerated computing and AI to enhance customer experience, stocking, pricing, and much more. 


RAPIDS hides the GPU-associated complexities and the behind-the-curtain communication protocols within the data center architecture. In doing so, RAPIDS makes it easy to execute analytics of company data science. 

3. Up to the Dask

More data scientists using technology like Dask – a flexible Python-based computing library that works with high-level languages and accelerates and improves development time without code changes. 

4. Turning Data Science into a Core Company Function

With accelerated computing, data science is becoming just as critical to businesses as the core functions of marketing, HR, or finance. As demonstrated by companies like Amazon and Walmart, data science helps companies attract customers, maximize profits and reduce waste. 


Accelerated computing brings in real-time data at the press of a few buttons, creating a distinct competitive edge for those companies whose tech team can harness the technology.


Taking advantage of accelerated computing requires the best available talent in data science. And you can find those exceptional candidates by contacting Synergy Systems today.


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