San Francisco, CA, USA
Dec 16, 2021   |  By Continual
First AI Company Built Natively for Cloud Data Warehouses Introduces Public Beta with Funding Led by Amplify Partners.
Dec 16, 2021   |  By Tristan Zajonc
Today we’re excited to announce the public beta launch of Continual, the first operational AI platform built specifically for modern data teams and the modern data stack. We’re also announcing our $4M Series Seed, led by Amplify Partners, and joined by Illuminate Ventures, Wayfinder, DCF, and Essence, as well as new partnerships with Snowflake and dbt Labs.
Dec 14, 2021   |  By Jordan Volz
Today we’re pleased to announce Continual Integration for dbt. We believe this is a radical simplification of the machine learning (ML) process for users of dbt and presents a well-defined path that bridges the gap between data analytics and data science. Read on to learn more about this integration and how you can get started.
Dec 13, 2021   |  By Jordan Volz
It’s easy to take continuous integration (CI) and continuous delivery/deployment (CD) for granted these days, but these have been transformational concepts that have drastically changed the face of software development over the past thirty years.
Dec 13, 2021   |  By Jordan Volz
While CI/CD is synonymous with modern software development best practices, today’s machine learning (ML) practitioners still lack similar tools and workflows for operating the ML development lifecycle on a level on par with software engineers. For background, follow a brief history of transformational CI/CD concepts and how they’re missing from today’s ML development lifecycle.
Oct 28, 2021   |  By The Continual Team
In our previous article, The Future of the Modern Data Stack, we examined the motivations of the modern data stack, its current state, and looked optimistically into the future to see where it is headed. If you’re new to the modern data stack, we highly recommend giving the aforementioned article a read. A question we often get from new adopters of the modern data stack is “What tech should we be looking into?”.
Oct 20, 2021   |  By Jordan Volz
A casual stroll through recent tech headlines in the past few years makes two things abundantly clear: investment in AI is at an all-time high, and companies really struggle to get value out of AI technology. At first glance, these ideas seem to be at odds with each other: why consider investing in a field that hasn’t lived up to the hype? If you dig into the details, you’ll notice that a gap exists between the development and production use of AI in many companies.
Oct 6, 2021   |  By Bethann Noble
Today, I’m excited to share that I’ve joined Continual as Head of Marketing. Continual is radically simplifying the path to operational AI with the first continual AI platform built for the modern data stack. More in a bit on what that means, but the “so what?” is about opening the door for more organizations to embed AI across their business at scale.
Sep 28, 2021   |  By Jordan Volz
Feature stores have arrived in 2021 as an essential piece of technology for operationalizing AI. Despite the enthusiasm for feature stores in high-tech companies, they are still absent from most legacy ML platforms and can be relatively unknown in many enterprise companies. We discussed how feature stores are critical to the data-first approach of next-gen ML platforms in our previous blog, but they are important enough to get their own treatment in a full article.
Aug 11, 2021   |  By Jordan Volz
We are roughly a decade removed from the beginnings of the modern machine learning (ML) platform, inspired largely by the growing ecosystem of open-source Python-based technologies for data scientists. It’s a good time for us to reflect back upon the progress that has been made, highlight the major problems enterprises have with existing ML platforms, and discuss what the next generation of platforms will be like.

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.