Continual is an AI platform that makes it easy to build predictive models on modern data stacks. It offers several key features and advantages, including:
Compatibility: It works seamlessly with popular cloud data platforms such as BigQuery, Snowflake, Redshift, and Databricks.
Simplified process: You don’t need complex engineering or MLOPS platforms to build models. You can use SQL or dbt declarations to create your models.
Shared features: It allows teams to accelerate model development by sharing features across different teams.
Continual improvement: The models continuously improve over time, ensuring that your predictions are always up-to-date.
Direct storage: Both your data and models are stored directly on the warehouse, making it easy to access them with operational and BI tools.
Continual has several use cases that cater to different business needs, such as:
Predicting customer churn to improve retention strategies.
Forecasting inventory demand for efficient supply chain management.
Estimating customer lifetime value to optimize marketing efforts.
Designed specifically for modern data teams, Continual is accessible to both SQL and dbt enthusiasts, as well as data scientists who integrate Python into their workflows.