New Research: Challenges and opportunities of MLOps

The use of artificial intelligence and machine learning (ML) to drive business transformation and reimagine customer experiences has become ubiquitous across industries and throughout organizations large and small. And one thing has become clear: the ability to deploy ML in and of itself is not a silver bullet to success. Today, an enterprise’s ability to leverage ML to its fullest capability has reached a critical juncture.

While many companies have built strong ML capabilities, far fewer have been able to deploy the majority of their ML models to production, leaving significant value on the table. Scaling ML to realize its maximum potential is a highly methodical process based on a set of standards, tools, and frameworks, broadly known as machine learning operations, or MLOps.

MLOps focuses on the entire life cycle of design, implementation, testing, monitoring, and management of ML models and has three primary goals: first, to develop a highly repeatable process over the end-to-end model life cycle, from feature exploration to model deployment in production. Another goal is to hide the infrastructure complexity from data scientists and analysts so that they can focus on their models and strategies, and the third goal is to develop MLOps in such a way that it scales alongside the number of models and modeling complexity without requiring an army of engineers.

MLOps helps to standardize and, to a degree, automate certain processes so engineers and data scientists can spend their time on better optimizing their model parameters and business objectives. MLOps can also provide important frameworks for responsible practices to mitigate bias, risk, and enhance governance.

In order to keep up with the fast-paced world of ML, organizations would do well to prioritize an MLOps strategy. As experts in this report agree, a targeted strategy can offer a reliable, nimble, and efficient approach to effectively embedding ML in a way that delivers value to the business, its employees, and its customers. But implementing MLOps is not without its challenges. It takes significant time, effort, and resources to develop the infrastructure needed to operationalize ML reliably, and at scale, across the enterprise in a repeatable way.

I’m excited to share that Capital One has worked in association with Harvard Business Review Analytic Services to produce a White Paper examining the vast and complex landscape of how organizations effectively use ML at scale, with an eye toward understanding the transformative potential of MLOps. Through interviews with ML consultants, analysts, academics, and practitioners, the findings elucidate the challenges and opportunities that come with MLOps, including how organizations can get the most out of their investments in ML with the right strategies. In addition to these insights, my colleagues and experts in machine learning, AI, and product development have added their own considerations and battle-tested recommendations for successful machine learning throughout the report.

Taken together, the insights and best practices offered in this report can set up an enterprise’s ML efforts for success by delivering the return on investment—and the differentiated value—that, when done right, ML uniquely makes possible.

Click through to our report landing page.