Making the most of MLOps
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.
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 outcomes, such as hospital readmission or disease progression.
“We were exploring data sets and models and talking with doctors to find out the features of the best models,” he says. “But to make these models actually useful we needed to bring them in front of actual users.”
That meant creating a mobile app that was reliable, fast, and stable, with a machine learning system on the back end connected via API. “Without MLOps we would not have been able to ensure that,” he says.
His team used the H2O MLOps platform and other tools to create a health dashboard for the model. “You don’t want the model to shift substantially,” he says. “And you don’t want to introduce bias. The health dashboard lets us understand if the system has shifted.”
Using an MLOps platform also allowed for updates to production systems. “It’s very difficult to swap out a file without stopping the app from working,” Zuccarelli says. “MLOps tools can swap out a system even though it’s in production with minimal disruption to the system itself.”
As MLOps platforms mature, they accelerate the entire model development process because companies don’t have to reinvent the wheel with every project, he says. And the data pipeline management functionality is also critical to operationalizing AI.
“If we have multiple data sources that need to talk to each other, that’s where MLOps can come in,” he says. “You want all the data flowing into the ML models to be consistent and of high quality. Like they say, garbage in, garbage out. If the model has poor information, then the prediction will itself be poor.”
But don’t think just because platforms and tools are becoming available that you can ignore the core principles of MLOps.
