Continual
Operational AI platform for modern data stack
What is it
Continual is an operational AI platform that offers a simplified approach to developing predictive models on modern data stacks. It supports popular cloud data platforms like BigQuery, Snowflake, Redshift, and Databricks, making it convenient to work with existing data infrastructure.
Key features
- Compatibility: Integrates seamlessly with popular data platforms.
- Simplified process: Allows model building using SQL or dbt declarations, eliminating the need for complex engineering or MLOPS platforms.
- Shared features: Facilitates collaboration by allowing teams to share features across models, accelerating development.
- Continual improvement: Models are automatically updated over time, ensuring predictions remain current.
- Direct storage: Data and models reside directly on the data warehouse, providing easy access for both operational and BI tools.
Pros
- Compatibility with popular data platforms.
- Simplified model building process.
- Accelerated development through shared features.
- Continual improvement of models over time.
- Easy access to data and models for operational and BI tools.
Cons
Information regarding potential drawbacks or limitations of Continual is not readily available in the provided context.
Summary
Continual is an operational AI platform designed to simplify the process of building predictive models on modern data stacks. Its key features include compatibility with popular data platforms, a simplified model building process, shared features, continual improvement, and direct storage. These features make Continual a valuable tool for data teams seeking to leverage predictive analytics in their business operations.
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