The power of MLOps to scale AI across the enterprise

This article is part of a VB special issue. To say that it's challenging to achieve AI at scale across the enterprise would be an understatement. An estimated 54% to 90% of machine learning (ML) models don't make it into production from initial pilots for reasons ranging from data and algorithm issues, to defining the business case, to getting executive buy-in, to change-management challenges. In fact, promoting an ML model into production is a significant accomplishment for even the most advanced enterprise that's staffed with ML and artificial intelligence (AI) specialists and data scientists. Enterprise DevOps and IT teams have tried modifying legacy IT workflows and tools to increase the odds that a model will be promoted into production, but have met limited success.