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Speed is the name of the game these days, especially when it comes to your organization’s ability to mine massive amounts of data for insights. The faster you can receive results from advanced...
In today’s competitive landscape, the ability to leverage data to quickly make informed decisions is critical.
But that alone is not enough.
In order to thrive, you also need to put data to work...
The rise in popularity of machine learning (ML) is leading to an influx of newcomers to the technology.
Many of these newcomers are under the assumption that an ML project is fairly straightforward...
Doing more with less.
This is a goal for every business and one of the cornerstones of DevOps practices. More development, more deployments, more ways to meet the needs of your customers faster.
When it comes to taking the plunge with machine learning (ML), it’s not uncommon for enterprises to feel skittish about costs.
Beyond the investment in data scientists, the sheer horsepower necessary...
The most routine data science tasks don’t need the sheer horsepower that deep learning models provide.
But as enterprises start using larger data sets, there are a growing number of complex tasks...
In early 2020, when Oxford Economics and NTT DATA surveyed a thousand business leaders about their plans for adopting artificial intelligence (AI), 96% responded they were actively researching AI...
According to Axios, in 2018 alone, Americans spent an estimated $3.65 trillion on healthcare. For some context, that’s more than the total GDP of Brazil.
With costs continually increasing, healthcare...
A joke you've likely heard before: "80 percent of time is spent preparing data. The other 20 percent is spent complaining about preparing the data."