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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...
Going multi-cloud can bring a number of benefits. Chief among them is flexibility in running your workloads.
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...
As more and more enterprises shift to DevOps, many of them quickly come to the realization that adopting a more agile approach to application development requires a fundamental change to their...
Enterprise data used to be relatively easy to govern.
Cost unpredictability is one of the main concerns for any organization moving to a cloud provider like Google Cloud Platform (GCP).
Unlike capital investments for on-premises infrastructure, cloud...
According to a study from Algorithmia, more than 80% of enterprises have increased their artificial intelligence (AI)/machine learning (ML) budgets over the past year.
It’s not uncommon for organizations that have migrated to Google Cloud Platform (GCP) to encounter sticker shock.
After all, one of the major selling points of GCP—and all public clouds—is the...