June 2, 2021
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. It’s built for modern data teams that want to leverage machine learning to drive revenue, streamline operations, and power innovative products and services without complex engineering.
Join me to learn more about Continual and see a live demo in our upcoming webinar on December 14th at 1 PM ET. Click here to register, and read on for more.
Continual is the missing AI layer for the modern data stack. It sits on top of cloud data warehouses such as Snowflake and provides a simple workflow to build predictive models that never stop learning from your data. These models can predict anything you want, from how likely a lead is to convert to whether a piece of equipment is likely to fail. Continual maintains these predictions in your data warehouse so they're immediately accessible to all your downstream applications. We call this a Continual AI Platform.
Unlike traditional machine learning engineering platforms, Continual is built to empower data and analytics professionals not simply machine learning engineers. If you like SQL and dbt, you’ll love Continual. Unlike most no-code AI tools, Continual is built for production, not exploratory workloads. It has a declarative workflow like SQL that radically simplifies operational AI/ML and delivers continually improving models and predictions.
Starting today, you can request a demo to experience Continual for yourself. While Continual is not yet generally available, we’re onboarding new users as fast as we can.
We founded Continual to make operational AI/ML pervasive across every organization.
Unfortunately, most companies are failing to scale AI/ML beyond a few scattered use cases. This is unsurprising. When every use case requires custom engineering, even something as basic as maintaining a customer churn prediction can quickly become a pipeline jungle spanning data processing, feature engineering, model training, inference, and monitoring. And simple no-code AI tools generally lack the operational characteristics required to power mission-critical applications.
We believe that simplicity and operational readiness don’t need to conflict. Until recently the big data and analytics ecosystem suffered from similar complexity. It was only in the last few years that the rise of the modern data stack – centered around scalable cloud data warehouses such as Snowflake – managed to simplify data and analytics infrastructure.
Today, there’s a vibrant ecosystem of operational data and analytics tools built for the modern data stack. Fivetran, Airbyte, and Segment have made data movement from databases and SaaS applications to the data warehouse easy. dbt has revolutionized data engineering and transformation. Monte Carlo Data, BigEye, and Soda have simplified data observability. And Census, Hightouch, and the emerging reverse ETL category have removed the need to build bespoke pipelines just to move data back into sales, marketing, support, and other operational tools.
We believe the rise of the modern data stack provides a similar opportunity to fundamentally reimagine operational AI/ML and it make accessible to every data professional. This isn’t about building a better machine learning engineering platform, but about building a Continual AI Platform to make operational AI/ML truly pervasive across every organization.
Continual is built natively for cloud data warehouses such as Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse Analytics, and Databricks Delta Lake. Cloud data warehouses have rightfully earned their place at the center of the data stack and are rapidly expanding their capabilities. By building directly on your existing data warehouse, you can not only immediately leverage all your production data but also the resulting predictions.
After connecting to your data warehouse, you can be up and running in minutes. Continual is a fully managed platform (with multiple deployment options) where data, features, and predictions live in your data warehouse. There’s no data replication or complex engineering required.
The secret sauce of Continual is a declarative data-first approach to operational AI/ML that is accessible to anyone familiar with SQL. The result is a simple workflow that makes it effortless to build continually improving predictive models and gives modern data teams superpowers.
While it’s not required, if you like dbt, you’ll love Continual. dbt is a development framework that combines modular SQL with software engineering best practices to democratize and scale data transformation workflows on the modern data stack. The global community of dbt users is thousands strong and growing, and Continual is pleased to provide dbt users a simple, scalable way to deploy ML use-cases on the modern data stack without the complexity of traditional MLOps solutions. dbt users simply annotate existing models to instruct Continual to register feature sets and automatically build and maintain predictive models.
Learn more about Continual and see a live demo in our upcoming webinar on December 14th at 1 PM ET. Click here to register.
Traditional AI platforms require users to manage complex data infrastructure and write bespoke ML pipelines for each case. We let users build predictive models using a declarative workflow that radically streamlines operational AI at scale.