Domino’s Enterprise MLOps platform simplifies the process of driving your data science work to fruition. Whether you want to push models to an inference application, publish data applications, or generate analytics reports, Domino helps you achieve that goal. Along the way, Domino’s user experience abstracts away the DevOps complexities and ensures you adhere to enterprise guardrails: security and governance.
In the realm of data applications and analytical reports, most data science efforts aim to improve the quality of business decisions. Often, this effort helps data scientists improve their ML workflows, helps data analysts derive insights from datasets, or provides business stakeholders with tools to generate reports that depend on complex data processing. Domino makes it easy for data scientists to collaborate and build data apps they can share with other stakeholders via interactive web apps or analytics reports via self-serve web forms.
This section describes how to configure, deploy, and manage your models and application to meet the needs of your data science projects.
- Deploy Models
Train models in Domino and deploy them for real-time inferences, long running inferences, batch scoring, or in-database scoring. You can also export them for hosting outside Domino.
- Publish Apps
Host an interactive app on Domino to share the outcome of your data science project with others.
- Publish Launchers
Domino Launchers allow data scientists to expose pre-canned technical analysis as a self-serve webpage for less technical stakeholders like business analysts.