As much as any big brand, Domino’s Pizza leans heavily on data to improve customer service. But the data science team at the world’s No. 1 pizza maker employs a secret ingredient to augment its decision-making: a machine learning (ML) hosting platform that helps it deliver models to production more quickly.
The team uses the platform to run, refine, and validate multiple ML and artificial intelligence models while reducing its reliance on IT for compute resources, which had historically been a critical friction point, according to Zack Fragoso, Domino’s manager of data science and AI.
“People like pizza and we process a lot of orders,” says Fragoso. “The missing piece to the puzzle was a way to get models into production.”
Harnessing analytics for business insights is a hard enough discipline, but data science practitioners must also contend with delays procuring everything from servers to software development environments from IT. Such delays pose problems for data science teams building potentially time-sensitive data models to augment their decision-making and achieve the desired business outcome, Fragoso says.
Domino’s journey to MLOps
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