Success Stories

Datatron Delivering Value & Generating ROI for Enterprise Clients & Partners

Domino’s Seeks Solution to AI/ML Logjam - Get’s Much, Much More With Datatron

Domino’s experienced the typical challenges of growing a nascent AI/ML program – slow model rollout due to repetitive, manual, & highly customized configurations, incompatibility between traditional software DevOps and MLOps, and challenging workflows between teams and different business units. Not to mention requirements from the “Risk & Compliance” teams.

Beyond improving their AI/ML workflow, Domino’s reaped numerous benefits after implementing Datatron as their MLOps and AI Governance platform, including:

  • 10x’d model deployment with existing resources – delivering more business value across the enterprise
  • Avoiding replicating headcount bloat & redundancy across different BUs to support AI
  • Quicker time to ROI for models (optimal store staffing, delivery routing, localized fees)
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It’s challenging to manage models, especially when there is a high rate of model growth each year. With Datatron, we were able to scale, manage and monitor all of our models on one centralized platform.

Zack Frogoso, Data Science & AI Manager, Dominos Pizza ​


With Datatron, Domino’s Achieved:

5x

Productivity
Improvement

80%

More Risk-Free Model
Deployments

10X

Model Deployment
Velocity
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Success Story: Global Bank Monitors 1,000’s of Models On Datatron

A top global bank was looking for an AI Governance platform and discovered so much more. With Datatron, executives can now easily monitor the “Health” of thousands of models, data scientists decreased the time required to identify issues with models and uncover the root cause by 65%, and each BU decreased their audit reporting time by 65%.

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Success Story: Domino’s 10x Model Deployment Velocity

Domino’s was looking for an AI Governance platform and discovered so much more. With Datatron, Domino’s accelerated model deployment 10x, and achieved 80% more risk-free model deployments, all while giving executives a global view of models and helping them to understand the KPI metrics achieved to increase ROI.

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5 Reasons Your AI/ML Models are Stuck in the Lab

AI/ML Executive need more ROI from AI/ML? Data Scientist want to get more models into production? ML DevOps Engineer/IT want an easier way to manage multiple models. Learn how enterprises with mature AI/ML programs overcome obstacles to operationalize more models with greater ease and less manpower.

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Life Cycle of Machine Learning Models

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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Unique Challenges Of Machine Learning Models In Production

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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Model Deployment

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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Our Latest Content

Self-Guided In-Product Tour (7 Mini-Videos)

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Experience “The Datatron” product for yourself in this self-guided series of seven, concise, mini-videos that highlight key features, like the “Model Catalog,” and “Health Dashboard,” as well as Use Cases for Data Scientists (Part III), ML Engineers/DevOps (Part IV), and AI Executives & BU/LOB leaders (Part VII). Enjoy! And, when you are ready, Book a Demo

Watch the Product Videos!