On the heels of announcing our $14.5M Series A and General Availability, we’re excited to be at the Data + AI Summit to unveil support for Continual on the Databricks Lakehouse. Increasingly, data and ML tool providers are embracing a data-centric approach to the ML workflow. The goal is to focus on what increasing drives ML – the data – compared to infrastructure, algorithms, or pipelines. At Continual we bet on data-centric AI from day one.

The modern data stack continues to attract companies who are looking for a quick onramp into the world of cloud-based analytics and/or actively modernizing their legacy data stacks. We've enumerated the benefits of the modern data stack in previous articles.

In this tutorial, we’re going to build an interactive customer Churn Insights Dashboard using the open-source Python framework, Streamlit, and the Continual predictions generated in Part 1: Snowflake and Continual Quickstart Guide. In Part 1, we connected Continual to Snowflake and used a simple dataset of customer information, activity, and churn status to build and operationalize a machine learning model in Continual to predict the likelihood of a customer churning.

‍ Today, we’re excited to announce the general availability of Continual, the missing AI layer for the modern data stack. We’ve also raised a $14.5M Series A, led by Innovation Endeavors and joined by Amplify Partners, Illuminate Ventures, Inspired Capital, Data Community Fund, Activation, New Normal, GTMfund, and angels Tomer Shiran, the founder of Dremio, and Tristan Handy, the founder of dbt Labs.