Domino’s dishes up AI/ML solutions at scale

Domino's is harnessing Datatron’s centralized platform to improve the performance of its AI and machine learning efforts. Leveraging Datatron to automate and standardize the deployment, monitoring, management, governance, and validation of AI models developed in any environment, Domino’s is able to improve in-store operations, find untapped revenue opportunities, and provide customers with an even greater experience.

Founded in 1960, Domino’s Pizza is the largest pizza company in the world based on retail sales. It ranks among the world’s top restaurant brands with a global enterprise of more than 17,200 stores in more than 90 markets. Domino’s had global retail sales of more than $14.3 billion in 2019, with over $7.0 billion in the U.S. and nearly $7.3 billion internationally.

“Machine learning models can provide significant value to an organization in several business applications, but without a solid ML operations pipeline, that value cannot be truly realized,” said Zack Fragoso, manager, data science & AI at Domino's. “We use Datatron as our enterprise MLOps and governance platform for mission critical projects.”

Domino’s wanted to be able to monitor models in real time and understand how predictions were changing over time. The company understood that as the truth or the reality changes, it needed a way for their models to be refreshed with new data. It also wanted to minimize the involvement needed by their data science resources as new models were being rolled out.

Recognizing this, Domino’s tapped Datatron’s smart visualization capabilities to create a system to operationalize its models in production and maintain them on a continuous improvement cycle. In this way, it can constantly monitor and update models and create a birds-eye, multi-level view of key metrics to monitor how the models perform in production.

As Domino’s continued to scale its internal data science team and business use cases leveraging ML, it realized the importance of the operations work involved in taking the models to an enterprise-grade production environment. By integrating Datatron’s API with multiple business applications, Domino’s was able to:

• Improve labor scheduling by forecasting the in-store labor required to meet service goals.
• Streamline vehicle routing by creating efficient routes for drivers.
• Refine identifying the locations for building new stores.

Through automation and standardization of machine learning operations, Datatron helps Domino’s increase the efficiency of managing multiple models at scale in order to optimize outcomes, reduce the efforts required by data scientists and other hard-to-find IT resources, and identify areas of growth. Using Datatron and Kubernetes, Domino’s was also able to allocate resources dynamically based on demand, saving both cost and resources.

For more information, go to www.datatron.com and www.biz.dominos.com.

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