Continual is an AI platform that streamlines predictive model building on today's modern data stacks. It offers a range of features and benefits, including:
Compatibility: It can be used with popular cloud data platforms like BigQuery, Snowflake, Redshift, and Databricks, making it easy to integrate into existing data infrastructure.
Simplified process: There's no need for complex engineering or MLOPS platforms. Instead, models can be built using SQL or dbt declarations, making it accessible to a wider range of users.
Shared features: Teams can accelerate model development by sharing features, which saves time and resources.
Continual improvement: Models improve over time, ensuring that predictions remain relevant and up-to-date.
Direct storage: Data and models are stored directly on the warehouse for easy access with operational and BI tools, streamlining workflows.
Continual can be used for a variety of business needs, including predicting customer churn, forecasting inventory demand, and estimating customer lifetime value. It's designed for modern data teams and is accessible to both SQL and dbt enthusiasts, as well as data scientists integrating Python.
With Continual, users can streamline their workflow and optimize their predictive model building process, making it an ideal choice for businesses looking to make more informed decisions based on data. The platform's compatibility, simplified process, shared features, continuous improvement, and direct storage make it a must-have for modern data teams.
