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Elevating MLOPs with Synthetic Data
Many an ambitious financial institution has, at some point, had to put a potentially game-changing AI project on ice. Perhaps you didn’t have the data you needed, or you couldn’t use the data you have. Perhaps your resources were spread too thin, or there was a mismatch of technical expertise between the business and data science departments. Perhaps the cycle of developing, testing, deploying, monitoring, reviewing, tweaking, retesting, and re-deploying your machine learning (ML) models into production was just too fraught with friction to make it work.
Whatever the reason, a brilliant concept slipped just out of reach.
Enter the dawn of MLOps: a collection of best practices designed to automate as much of the ML lifecycle as possible, bridging the gap between data science and business, developers and operations. It’s an approach that’s making waves: Deloitte predicts that the sector will be worth $4 billion by 2025.