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Continual

About

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 AI 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
  • Screenshots

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