Domino Data Lab

Headquartered in San Francisco, Domino is an enterprise MLOps platform trusted by over 20% of the Fortune 100. Their products enable thousands of data scientists to develop better medicines, grow more productive crops, adapt risk models to major economic shifts, build better cars, improve customer support, or simply recommend the best purchase to make at the right time. At Domino, their mission is to unleash the power of data science to address the world’s most important challenges.

Domino provides a central system of record that tracks all data science activity across an organization and acts as an orchestration layer on the AWS compute/storage foundation. The result is a platform that all stakeholders can rely on. Data scientists get flexibility and scalable compute. IT gets visibility into and management over resource consumption. DevOps gets a scalable deployment platform. Management gets a reliable, repeatable process for implementing model-driven business programs.

Enterprise organizations that have data science, machine learning or research practices are a perfect fit for Domino's products.

• Data Science Platform: Domino centralizes data science work and infrastructure across the enterprise for collaboratively building, training, deploying and managing models — quicker and more efficiently. With Domino, data scientists can innovate faster, teams reuse work and collaborate more, and IT teams can manage and govern infrastructure.
• Model Monitor: The Domino Model Monitor (DMM) creates a “single pane of glass” to monitor the performance of all models across an entire organization. Data scientists can focus on value-added projects and receive alerts when production models degrade. Data drift, model quality, and other health statistics are provided in an easy-to-understand dashboard so users can assess business impacts and initiate proactive actions.

General qualifiers

• How many data scientists do you have (>20)?
• What languages do they code in (Python, SAS, MATLAB, R, Julia, etc.)?

Identified pain(s) that is caused or exacerbated by their current tech environment

• Do you have difficulty getting access to appropriate GPU or compute resources?
• Are you scheduling GPU access over slack or email?
• Are your data scientists being forced to do DevOps work instead of research?

Organizational issues

• Are you building an analytics Center of Excellence?
• Do you have difficulty with knowledge management (i.e., knowing where code or data comes from when developing a model)?
• Do you have trouble collaborating between and amongst teams for data science work?
• Are you in a regulated industry?
• Is reproducibility of model development important?