The role of data in DevOps is changing, facilitated by the introduction of powerful new generative artificial intelligence engines such as ChatGPT.

The success of DevOps in modern, cloud-native environments has given rise to platform engineering, and among the many byproducts of this trend is a growing potential for AI and machine learning to be used in specific, data-driven scenarios.

“Machine learning here is the interesting new variable in this equation,” said Matt Butcher (far right), chief executive officer of Fermyon Technologies Inc. “We’ve learned to feed data … into the model and have it figure out what the relationships are. And then the query language becomes chat, the spoken word. We’re living through yet another one of those change moments.”

Butcher spoke with theCUBE industry analyst John Furrier (second from left) at the KubeCon + CloudNativeCon Europe event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. He was joined by Sanjeev Mohan (far left), principal at SanjMo, and Justin Cormack (second from right), chief technology officer of Docker Inc., and they discussed how the expanded use of AI is changing the DevOps landscape. (* Disclosure below.)

Disruption and innovation

The rise of generative AI engines, such as ChatGPT, has led to questions within the industry about whether major database players could get disrupted by adoption of new intelligence tools within the enterprise world.

“Depending on who you talk to, you find out either ChatGPT produces pristine code or it screws it up,” Mohan said. “I don’t see any company, like Snowflake and the database companies, getting disrupted at this point. ChatGPT is an amazing way to get started but not to finish the job.”

Nevertheless, the DevOps world is encountering a wave of innovation driven by new data management approaches. This has influenced changes in systems thinking, including a re-evaluation of the Twelve-Factor App.

Twelve-Factor is a methodology for building software-as-a-service applications. It includes the use of backing services with a focus on databases and storage layer processes for accessing data using well-documented APIs.

“If you look at the Twelve-Factor app from the beginning of containers, the one about data is the one that doesn’t really apply anymore,” Cormack said. “We’re seeing a huge amount of innovation in the data space.”

Transformation of the data landscape for DevOps is also leading to a re-evaluation of the roles played by both cloud and on-premises operations in providing critical IT services. The cloud started largely as a simulation of the data center, according to Butcher, but that has evolved into a different approach for enterprises.

“We’re starting to understand when we can rent/lease somebody else’s computing power, and we can store vast amounts of data in there, we can build things differently than when we have to pay for the hardware ourselves,” Butcher said. “Then we have other things where we start saying: ‘Wait, some of this we have to move back closer to the user.’ That means we have to rethink a lot of the architecture.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the KubeCon + CloudNativeCon Europe event:

(* Disclosure: This is an unsponsored editorial segment. However, theCUBE is a paid media partner for KubeCon + CloudNativeCon. Neither Red Hat Inc. nor other sponsors of theCUBE’s event coverage have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE