There’s a lot of potential in Machine Learning (ML). Unfortunately, there are also a number of obstacles companies hit when it comes to realizing that potential.

By some estimates, close to 90% of Machine Learning (ML) projects developed by data scientists fail. This translates into wasted resources and the potential of ML being deployed at a snail’s pace.

It’s no secret there are substantial costs in running your own datacenter—both monetarily and for the environment.

For close to a decade now, Hadoop has provided a workable solution for on-premises Big Data workloads. But times change, and now more and more companies are turning to the cloud for their Big Data...

The transition to the public cloud is not easy.

From Dipping Toes into Containerization to Diving into K8s

When and iconic gaming company was ready to go all in on Kubernetes, they partnered with Redapt for help.

In a nutshell

The idea of moving data workloads to the cloud can be daunting, especially if your company has always relied upon on-premises solutions.

Why? Because while the end goals of utilizing data may be the...

A Big Data Solution to a Big Insurance Problem

When healthcare technology company Zelis needed a Big Data solution to easily identify fraudulent claims, Redapt’s data scientists provided them with a...

Speed to market is a competitive edge. If your company is still employing traditional software development processes—monolithic apps, waterfall development, and slow deployments—then embracing...

Edge computing is the process of bringing compute and storage closer to where it’s needed. This has some obvious benefits, including: