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The combination of Continual and Snowflake empowers customers to operationalize AI at scale to drive revenue, operate more efficiently, and build innovative products and services. Continual is the first operational AI platform built natively for cloud data warehouses and the modern data stack. Snowflake’s cloud data platform enables users to work with their data, virtually without limits on scale, performance, or flexibility. Operationalizing ML use cases has never been easier or faster.

Step through an easy example of how to build an ML solution with Continual and Snowflake.

Continual is proud to announce that we are now SOC 2 Type 1 certified and compliant, with SOC 2 Type 2 in progress.

At Continual, we believe that feature engineering is the most impactful part of the ML process and the one that should have the most human intervention applied to it. In this article, we’ll break down feature engineering into several different concepts and provide our guidance on each. 

In this use case deep dive, learn how to tackle the customer churn use case using Continual, Snowflake, and dbt.

Model performance depends on the metric being used. Understanding the strengths and limitation of each metric and interpreting the value correctly is key to building high performing models. In part 1, we cover four evaluation metrics commonly used for regression problems and demonstrate how to use them when building models on Continual.

We’re excited to announce our public beta and $4M in seed, led by Amplify Partners, to bring operational machine learning and AI to the modern data stack.

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.

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. In this article, we'll explore how Continual provides a streamlined operational AI workflow that works seamlessly with the CI/CD approach and allows you to productionalize your AI workflows in a snap.

While many have called for stronger adherence to software development best practices in machine learning (ML) and artificial intelligence (AI) as well, today’s ML practitioners still lack simple tools and workflows to operate the ML deployment lifecycle on a level on par with software engineers. This article takes a trip down memory lane to explore the benefits of the CI/CD toolset and the detriment of their unfortunate absence in today’s ML development lifecycle. 

Continual presents a who's who of the Modern Data Stack. Interested in the emerging trends, or just looking to get started? We've got it all covered in this article.

Why do some companies struggle to operationalize AI and others succeed? In this article, we will look at the common pitfalls to avoid and illuminate a path to success with operational AI.

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.

Feature stores are driving a revolution in machine learning and are a key component to operationalizing AI. In this blog we'll discuss what they are, how they came to be, and why you need one.

Data-First AI is poised to drive the next generation of ML platforms. Read our take on how the ML platform landscape has evolved over the years and why the data-first approach is here to stay.

We've been thinking a lot about the future of the modern data stack. Here are five predictions about where we think it will go.

Today, I’m extremely excited to introduce Continual. Continual is the easiest way to maintain predictions – from customer churn to inventory forecasts – directly in your cloud data warehouse. Read more.