Domino – A Data Science Workbench For Data Scientists

Originally, dominos were used as a form of gaming. The game originated in Italy in the early 18th century. It then spread to France and England during the mid-18th century. The name domino first appeared in the Dictionnaire de Trevoux in 1771. It was used in different games, including Tien Gow and Che Deng.

The domino is a tile-based game. Typically, each domino is made up of 28 pieces. These pieces are often made of ivory or dark hardwood. The dominoes are also marked with a series of spots. The pieces can be stacked in long rows or flipped over in order to make the next domino. The game is often played by two or four people. Each player draws a number of pieces required for the game, and the first player in line knocks down the domino that he draws. The next domino in line is knocked down, and so on. The person who has the fewest pips wins the game.

The domino has an obscure origin. The word “domino” was originally a shortened version of the Latin word, doma. It is used to refer to a small rectangular block, usually twice as long as wide, that is used for gaming. The domino’s first recorded use is in Italy, and it became a popular fad in France in the mid-18th century.

The domino was introduced to England in the late 1700s. Its popularity spread across Europe, and it became popular in the United States in the 1860s. Although some dominoes are blank, others have markings on the sides and are usually marked with a series of spots.

In the United States, the game is commonly called “boneyard”. A person who wins the game must leave behind any pieces that are not used. Several variants of the game are also played. Most of these games are adaptations of card games. They include solitaire domino games and trick-taking domino games.

The Domino data science workbench is a platform for data scientists that provides a governed, reproducible environment, elastically scaled compute, and tools to deploy and publish models. Domino’s platform makes it easier for teams to collaborate, and helps data scientists work more productively. It also simplifies the learning curve for data scientists who are new to the platform.

The Domino data science workbench includes tools that allow data scientists to build, scale, and publish machine learning models. Domino models can be exported to Docker images, and they can also be hosted as REST API endpoints. The Domino Data Lab also makes it easy to interact with models by providing interactive apps that non-technical users can easily use. These apps can be written with Flask, Shiny, or Dash.

The Domino data science workbench also provides tools for centralized infrastructure management and security. This enables easy scaling, while reducing the learning curve for DevOps. Moreover, it provides a governed, reproducible environment that enables easy comparison of results. It also allows data scientists to run hundreds of machine learning experiments in parallel. This helps data analysis teams build custom toolsets that match their unique workflows.