SAN FRANCISCO--(BUSINESS WIRE)--Domino Data Lab (Domino), a leading solution for data science acceleration, announced today that it has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status. This designation recognizes Domino for providing business analysts, data scientists and ML practitioners with automated, cutting-edge tools to create and deploy predictive models on AWS.
Managing "environments" (i.e., the set of packages, configuration, etc.) is a critical capability of any Data Science Platform. Not only does environment setup waste time on-boarding people, but configuration issues across environments can undermine reproducibility and collaboration, and can introduce delays when moving models from development to production. This post describes how Domino uses Docker to address these environment issues, and more specifically how this approach can integrate with common package management solutions like Anaconda. Domino Compute Environments let data scientists manage Docker images with arbitrary software and configuration. These environment definitions are shared, centralized, and revisioned -- and when Domino runs your code (during model training or for deployment) across its compute grid, your code runs in your environment.
If you order a pizza from Domino's, you might be getting it with a free side of AI. The pizza giant is determined to make big data and AI cornerstones of a company that has typically focused more on pizza stones. Now, Nvidia is highlighting how Domino's is leveraging the power of data to deliver valuable insights in addition to pizza. Zack Fragoso, a data science and AI manager at Domino's, explained how the company had grown its data science team exponentially – a move "driven by the impact [the team] had on translating analytics insights into action items for the business team." Domino's made its first public foray into AI with "Points for Pie," a Super Bowl ad stunt that allowed customers to send a smartphone picture of (any) pizza to Domino's, earning points that could be used for free pizza.
A domino portrait is an approximation of an image using a given number of sets of dominoes. This problem was first stated in 1981. Domino portraits have been generated most often using integer linear programming techniques that provide optimal solutions, but these can be slow and do not scale well. We demonstrate a new approach that overcomes these limitations and provides high quality portraits. Our software combines techniques from operations research, artificial intelligence, and computer vision. Starting from a randomly generated template of blank domino shapes, a subsequent optimal placement of dominoes can be achieved in constant time when the problem is viewed as a minimum cost flow. The domino portraits one obtains are good, but not as visually attractive as optimal ones. Combining techniques from computer vision and local search we can improve our portraits to be visually indistinguishable from those generated optimally.