Our customers are at the forefront of some of the most interesting data in the world, using SingleStoreDB to push the boundaries every day. To do this well, they leverage the expertise of our Support Engineering Team, composed of technical subject-matter experts on the frontlines of critical customer issues. To accurately identify the source and solution of an issue, this team will take the time to learn about the customer's business and systems while helping to improve their fundamental SingleStoreDB and database operational knowledge. This often requires additional research and time spent on learning new technologies and tools outside SingleStoreDB while also being deeply engaged with multiple departments including development teams, query performance engineering, product management, infrastructure SREs, etc. SingleStore is one platform for all data, built, so you can engage with insight in every moment. SingleStore is venture-backed and headquartered in San Francisco with offices in Sunnyvale, Seattle, Boston, London, Lisbon, Bangalore, Dublin and Kyiv.

This article will show how to use SingleStoreDB with MindsDB using Deepnote. We'll create integrations within Deepnote, load the Iris flower data set into SingleStoreDB, and then use MindsDB to create a Machine Learning (ML) model from the Iris data stored in SingleStoreDB. We'll also make some example predictions using the ML model. Most of the code will be in SQL, enabling developers with solid SQL skills to hit the ground running and start working with ML immediately. The notebook file used in this article is available on GitHub.