The Ever-growing Importance of Machine Learning Operations

Ever-increasing enterprise investments are driving AI to explosive growth, with 86% of global companies prioritizing AI and ML over other initiatives. AI and machine learning initiatives are the gifts that keep on giving, simultaneously increasing top-line revenue and decreasing bottom-line costs. But to meet this scale in demand, organizations have to navigate a myriad of new challenges, from IT governance and security, to data security, privacy, and tax regulatory compliance. And automation is the key to AI success. 

Trends in Influence in IT and Infrastructure

With the enthusiasm that drives AI adoption comes the equal trouble of long-term deployment. In fact, 87% of organizations struggle with lengthy deployment timelines, a further 59% take over a month to deploy a trained model into production. And Gartner finds that only 53% of models make it into production.

Machine learning operations (MLOps) help curb this problem. Through repeatable and efficient workflows, this approach introduces IT early on, integrating throughout existing tools and enabling automation by scaling. MLOps provides a solid foundation to connect stakeholders throughout the process and provides IT teams with efficient and scalable workflows to drive enterprise AI/ML initiatives. 

Key Developments in ML Lifecycle Automation

DataRobot’s MLOps provides organizations with a single location from where to deploy, manage, and govern their machine learning models. Individuals across teams are able to contribute to the scaling and management of models in production, supported by DataRobot’s advanced security and governance frameworks. 

The platform is optimized to help organizations to maximize their ROI. As an origin-agnostic platform, it’s able to work with models regardless of their original languages or environments. And not only that but the platform’s ability to automate ML deployment and integrate with pre-existing tools, alongside its accommodations for continuously changing conditions, empowers teams to collaborate and scale their trusted models in production. 

Catching Up and Keeping Up

In order to remain an active competitor, companies are backing this agenda with practical investments. And as governance issues crop up as organizations take manual routes to production ML, automation becomes key to reducing them. As long as their efforts, through MLOps, remain aligned with IT capabilities, they can continue to push for desired business results.

Read the second blog of the series, we’ll dive deeper into DataRobot’s Machine Learning Operations capability, and its transformative effect on the machine learning lifecycle.