Implementing MLOps requires significant cultural shifts to loosen firmly rooted, siloed ways of working and focus teams on creating a factory-like environment around AI development and management. Building an MLOps capability will materially shift how data scientists, engineers, and technologists...

A best-in-class framework for ways of working, often called MLOps (machine learning operations), now can enable organizations to take advantage of these advances and create a standard, company-wide AI “factory” capable of achieving scale. Since MLOps is relatively new and still evolving,...

While a robust risk-management program driven by legal, risk, and AI professionals must underlie any company’s AI program, many of the measures for managing these risks rely on the practices used by AI teams. MLOps bakes comprehensive risk-mitigation measures into the AI application life cycle.

Newer roles needed on AI teams have emerged, like that of the machine learning engineer who is skilled in turning AI models into enterprise-grade production systems that run reliably. To build out its ML engineering team, a North American retailer combined existing expertise of internal IT develo...

The availability of technical talent is one of the biggest bottlenecks for scaling AI and analytics in general. When deployed well, MLOps can serve as part of the proposition to attract and retain critical talent. Most technical talent gets excited about doing cutting-edge work with the best tool...

Among the key performance metrics CEOs can champion are the following:

• the percentage of models built that are deployed and delivering value, with an expectation of 90 percent of models in production having real business impact
• the total impact and ROI from AI as a measurement ...

Embedding AI across an enterprise to tap its full business value requires shifting from bespoke builds to an industrialized AI factory. 

For AI to make a sizable contribution to a company’s bottom line, organizations must scale the technology across the organization, infusing it in core bus...

One of the fundamental litmus tests for impact is the degree to which goals are shared across business leaders and the respective AI, data, and IT teams. Ideally, the majority of goals for AI and data teams should be in service of business leaders’ goals. Conversely, business leaders should be ab...

Organizations often invest significant time and money in developing AI solutions only to find that the business stops using nearly 80 percent of them because they no longer provide value—and no one can figure out why that’s the case or how to fix them. In contrast, we find that companies using co...

As in any technology transformation, CEOs can break down organizational barriers by vocalizing company values and their expectations that teams will rapidly develop, deliver, and maintain systems that generate sustainable value. CEOs should be clear that AI systems operate at the level of other b...

Moving AI solutions from idea to implementation takes nine months to more than a year, making it difficult to keep up with changing market dynamics. Even after years of investment, leaders often tell us that their organizations aren’t moving any faster.

Companies applying MLOps can go from ...

Achieving productivity and speed requires streamlining and automating processes, as well as building reusable assets and components, managed closely for quality and risk, so that engineers spend more time putting components together instead of building everything from scratch.