Content area
Full Text
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.
IDC predicts that by 2024 60% of enterprises would have operationalized their ML workflows by using MLOps. And when companies were surveyed about the challenges of AI and ML adoption, the lack of MLOps was a major obstacle to AI and ML adoption, second only to cost, Subramanian says.
Here we examine what MLOPs is, how it has evolved, and what organizations need to accomplish and keep in mind to make the most of this emerging methodology for operationalizing AI.
The evolution of MLOps
When Eugenio Zuccarelli first started building machine learning projects several years ago, MLOps was just a set of best practices. Since then, Zuccarelli has worked on AI projects at several companies, including ones in healthcare and financial services, and he’s seen MLOps evolve over time to include tools and platforms.
Today, MLOps offers a fairly robust framework for operationalizing AI, says Zuccarelli, who’s now innovation data scientist at CVS Health. By way of example, Zuccarelli points to a project he worked on previously to create an app that would predict adverse...