2 years ago

Category: Kubernetes, Infrastructure, artificial-intelligence

What we saw was this convergence of machine learning, simulations, and big data processing, where the big workflow or application you’re trying to build in the end needs to stitch together various aspects of these capabilities and that the struggle is in executing the scalability on the cloud,” said Priya Nagpurkar, director of cloud platform research at IBM Research.

That’s why, from a cloud and platform and runtimes perspective, what we said is, ‘How can we evolve our cloud platform to cater to these new emerging workloads?'”

CodeFlare looks to simplify this process by narrowing everything down to a single runtime using Python, the language that already serves as a common tool in data science, and by handling the task of scaling those processes as needed.

In its announcement, IBM specifically calls out the company’s new serverless platform, IBM Cloud Code Engine, as well as Red Hat OpenShift, as cloud platforms that CodeFlare easily deploy to, but Nagpurkar explained that Kubernetes is the common necessary substrate.

As with many developments of this nature, CodeFlare is described as providing a consistency for data scientists that will allow them to “focus more on their actual research than the configuration and deployment complexity.”