San Francisco, CA, USA
Jun 28, 2022   |  By Michael Ernest
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.
Jun 28, 2022   |  By Jordan Volz
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.
Jun 15, 2022   |  By Brendan McKenna
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.
Jun 8, 2022   |  By Tristan Zajonc
‍ 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.
Apr 12, 2022   |  By Jordan Volz
Welcome to the Spring 2022 Edition of the Modern Data Stack Ecosystem. In this article, we’ll provide an in-depth look at the Modern Data Stack (MDS) ecosystem, updated from our Fall 2021 edition. We also highly recommended our article, The Future of the Modern Data Stack, to anyone who is new to the MDS and wants to learn about its history.
Apr 5, 2022   |  By Brendan McKenna
Data infrastructure is rapidly growing and evolving along with infrastructure for AI/ML, with the latter growing largely independent from the former. An emerging generation of AI/ML tooling emphasizes data-centric versus model-centric approaches to the ML development lifecycle. These tools recognize that data is the foundation for AI and seek to open opportunities for all data professionals to participate by eliminating the unnecessary complexity of traditional model-centric solutions.
Apr 4, 2022   |  By Brendan McKenna
This guide will show you how to easily add Continual as the AI layer to your modern data stack with Snowflake at the core. The intention is to provide an introduction to using Continual on Snowflake. After completing this tutorial, users are invited to try more advanced examples. We are going to demonstrate connecting Continual to Snowflake, building feature sets and models from data stored in Snowflake, and analyzing and maintaining the predictive model continuously over time.
Mar 16, 2022   |  By Martin Suchanek
Continual is proud to announce that we are now SOC 2 Type 1 certified and compliant and SOC 2 Type 2 in progress. This certification is a publicly visible milestone that demonstrates our core commitment to keeping your data secure. We expect to make additional announcements around our security certification efforts over the coming months. Beyond third party attestations, Continual is built from the ground up with data security and governance in mind.
Mar 3, 2022   |  By Jordan Volz
Feature engineering is a crucial part of any ML workflow. At Continual, we believe that it is actually the most impactful part of the ML process and the one that should have the most human intervention applied to it. However, in ML literature, the term is often overloaded among several different topics, and we wanted to provide a bit of guidance for users of Continual in navigating this concept.
Feb 8, 2022   |  By Jordan Volz
In this article, we’ll take a deep dive into the customer churn/retention use case. This should contain everything needed to get started on the use case, and enterprising readers can also try this out for themselves in a free trial of Continual, following the customer churn example in the linked github repository.

Maintain continually improving predictions – from customer churn to inventory forecasts – directly in your data warehouse. No complex engineering required.

Continual sits on top of your cloud data warehouse and makes it easy to build, deploy and maintain predictive models that never stop learning from your data. These models can predict anything, from customer LTV to equipment failure. Try it for free.

Built for modern data teams:

  • Zero Infrastructure: Maintain features and predictions directly in your data warehouse without new infrastructure.
  • Shared Feature Store: Share feature definitions defined in SQL across your team to accelerate model development.
  • Declarative AI Engine: Build state-of-the-art models that leverage all your data without writing code or pipelines.
  • Feature Time Travel: Avoid data leakage with point-in-time correct features from a collaborative feature store.
  • dbt Integration: Leverage your existing dbt models and workflow to radically reduce the complexity of operational AI.
  • CI/CD Friendly: Fully govern features, models, and policies with a declarative workflow that enables GitOps.

Operational AI for Modern Data Teams.