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Apps 2021-06-15

Continual

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Cloud-based predictive modeling using SQL.

Overview

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Continual is an operational AI platform designed to help build predictive models on modern data stacks. Users can easily create predictive models on data stored on popular cloud data platforms like BigQuery, Snowflake, Redshift, and Databricks.

With Continual, there is no need for complex engineering or infrastructures such as MLOPS platforms, as it simplifies the process of building and maintaining predictive models through simple SQL or dbt declarations.

Features can also be shared across teams, which accelerates model development. Models built on Continual improve continually, so predictions are always up-to-date.

Data and models are stored directly on the warehouse, making it easily accessible to operational and BI tools. Continual is versatile and can be useful in a business to predict customer churn, inventory demand, and customer lifetime value.

Continual is designed for modern data teams who love SQL and dbt, but it is also equally accessible to data scientists who can extend the platform by integrating Python.

Continual's declarative approach to AI makes it easy to work with for analytics and data teams.

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Continual was manually vetted by our editorial team and was first featured on March 17th 2023.

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Pros and Cons

Pros

Cloud-based predictive modeling

Uses SQL for app creation

Works with BigQuery, Snowflake, Redshift, and Databricks

No need for complex infrastructure

Models improve continually

Data and models stored on warehouse

Easily accessible to operational and BI tools

Suitable for customer churn, inventory demand, and customer lifetime value predictions

Equally accessible to data scientists

Facilitates Python integration

Shared features accelerate model development

Simplifies process of building and maintaining predictive models

Models are up-to-date

Centralized feature store

Extensible with Python

CI/CD friendly

GitOps workflow support

Zero infrastructure requirement

Works natively with modern cloud data platforms

Declarative model and feature definition

dbt integration

Cons

SQL-centric

Limited to cloud data platforms

Dependency on modern data stacks

No MLOPS infrastructure

Limited extensibility (Python only)

Dependent on dbt compatibility

Not suitable for traditional data management systems

Data must be on the same warehouse

No mention of multilingual support

Dependent on continuous access to data warehouse

Q&A

What is Continual?

Continual is an operational AI platform created to assist in building predictive models on data stacks. It eliminates complex engineering requirements, simplifying the predictive models' building and maintenance process through SQL or dbt declarations.

What can I do with Continual?

With Continual, you can create and enhance predictive models on data stored on various popular cloud data platforms such as BigQuery, Snowflake, Redshift, and Databricks. It enables you to easily predict customer churn, inventory demand, and customer lifetime value.

What types of predictive models can I build with Continual?

Continual allows you to build diverse predictive models. Businesses can predict parameters like customer churn, inventory demand, and customer lifetime value. These predictive models can be enhanced and are always up-to-date, continually improving their accuracy.

Which cloud platforms is Continual compatible with?

Continual is compatible with popular cloud data platforms like BigQuery, Snowflake, Redshift, and Databricks. It sits on top of these platforms to build, deploy, and enhance predictive models.

How do I create predictive models in Continual?

To create predictive models in Continual, you can utilize SQL or dbt declarations. Users can define features and models declaratively and employ SQL or dbt or extend with Python. This declarative approach simplifies the process, allowing the best use of your data without needing to write code or pipelines.

Is Continual user-friendly for people who know SQL and dbt?

Absolutely, Continual is designed to be user-friendly for individuals knowledgeable in SQL and dbt. Its declarative approach and the ability to connect to your existing cloud data warehouse streamline the process, making it an ideal tool for modern data teams who prefer SQL or dbt.

Is there a need for complex engineering to use Continual?

No, there is no need for complex engineering to use Continual. It enables the building of predictive models that never stop improving without any complex engineering due to its declarative approach to AI.

How can I integrate Python into Continual?

In Continual, data scientists can extend the platform by integrating Python. This provides versatility, uniting analytics and AI teams with full extensibility of Continual's declarative AI engine.

What is the role of SQL or dbt in Continual?

In Continual, SQL or dbt plays a critical role. Users can define features and deploy state-of-the-art Machine Learning models using SQL or dbt. Furthermore, feature definitions created in SQL can be shared across your team to speed up model development.

Can predictive models in Continual be shared across teams?

Yes, models built in Continual can be shared across teams. This accelerates the development of models and encourages teamwork and collaboration.

Are predictions from Continual always up to date?

Yes, the models built on Continual improve continually and as a result, predictions are always up-to-date. This leads to more accurate predictions.

Where are data and models from Continual stored?

Data and models from Continual are stored directly on your warehouse. This direct storage in the data warehouse makes it easily accessible to operational and BI tools.

How can Continual help me predict customer churn and inventory demand?

Continual's predictive models can help in preempting customer churn and forecasting inventory demand. By selectively analyzing relevant data, predictive models can isolate key parameters promoting customer churn or impacting inventory demand, driving strategic decision-making based on these forecasts.

Does Continual offer a free trial?

Yes, Continual offers a free trial. You can experience the benefits of automated AI for the modern data stack and decide if it's the right solution for your specific needs.

How do I request a demo of Continual?

To request a Continual demo, you can visit their website and fill out the demo request form. You need to provide your First Name, Last Name, Company Name, and Work Email. After submitting the form, the Continual team will reach out to you very shortly.

Can Continual be used by data teams and data scientists alike?

Yes, Continual can be used by both data teams and data scientists. For those familiar with SQL and dbt, Continual is highly accessible. Additionally, data scientists can extend the platform by integrating Python, ensuring adaptability and wide applicability.

What are some use cases for Continual in business operations?

In business operations, Continual can be used to predict demand and inventory, which can help with efficient management and reduction of costs and waste. It can also be used to predict sales and revenue for budgeting and planning purposes. Essentially, Continual can be used in any situation where predictive modeling can enhance decision-making and operational efficiency.

Does Continual provide a centralized feature store?

Yes, Continual provides a centralized feature store. You can share feature definitions created in SQL across your team in a centralized manner, enhancing collaboration and accelerating model development.

Can predictive models in Continual continually improve independently?

Yes, predictive models in Continual can continually improve independently. The platform is designed to ensure that your predictive models never stop learning and improving, leading to continually updated and more accurate predictions.

How does Continual simplify the process of building and maintaining predictive models?

Continual simplifies the process of building and maintaining predictive models through a declarative approach to AI. Users define features and models declaratively, eliminating the need to write complex code or pipelines. Additionally, it eliminates the need for MLOPS platforms, storing all data and models directly on the warehouse for easy accessibility.