Continual

Further Information

Key Features

• Continual AI Tool for Modern Data Teams
• Compatibility:
• Works with popular cloud data platforms like BigQuery, Snowflake, Redshift, and Databricks
• Simplified Process:
• No need for complex engineering or MLOPS platforms
• Build models using SQL or dbt declarations
• Shared Features:
• Accelerate model development by sharing features across teams
• Continual Improvement:
• Models improve over time, ensuring up-to-date predictions
• Direct Storage:
• Data and models stored directly on the warehouse for easy access with operational and BI tools
• Use Cases:
• Predict customer churn to improve retention strategies
• Forecast inventory demand for efficient supply chain management
• Estimate customer lifetime value to optimize marketing efforts
• Designed for Modern Data Teams:
• Accessible to both SQL and dbt enthusiasts as well as data scientists integrating Python.

Use Cases

• Continual is a tool designed for modern data teams that can be accessed by both SQL and dbt enthusiasts as well as data scientists integrating Python.
• It can predict customer churn to improve retention strategies.
• Continual can forecast inventory demand for efficient supply chain management.
• It can estimate customer lifetime value to optimize marketing efforts.