When companies first start deploying artificial intelligence and building machine learning projects, the focus tends to be on theory. Is there a model that can provide the necessary results? How can it be built? How can it be trained?
But the tools that data scientists use to create these proofs of concept often don’t translate well into production systems. As a result, it can take more than nine months on average to deploy an AI or ML solution, according to IDC data.
“We call this ‘model velocity,’ how much time it takes from start to finish,” says IDC analyst Sriram Subramanian.
This is where MLOps comes in. MLOps — machine learning operations — is a set of best practices, frameworks, and tools that help companies manage data, models, deployment, monitoring, and other aspects of taking a theoretical proof-of-concept AI system and putting it to work.
“MLOps brings model velocity down to weeks — sometimes days,” says Subramanian. “Just like the average time to build an application is accelerated with DevOps, this is why you need MLOps.”
By adopting MLOps, he says, companies can build more models, innovate faster, and address more use cases. “The value proposition is clear,” he says.