Redapt | AI/ML (2)
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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."
It’s one of the ironies of business: the more popular your product becomes, the harder it is to keep up with demand.
Even products we don’t normally think of as dependent upon traditional logistics,...
Editor’s Note: This post was originally published on May 6, 2020 and has been recently updated to include the latest insights and best practices.
Over the past year, the need to expand remote work capabilities has forced a great many enterprises to accelerate their cloud adoption plans.
Note: This post was originally published on March 19, 2020, and has been updated with new information.
Information, as they say, is power.