Automi or Continual? A Comprehensive Review

Transparency emphasis

Access to training datasets

Provides biases and limitations

Data analysis tools

Anomaly detection tools

Trend forecasting tools

Easy adaptability to data

Designed by expert team

Community-driven development

Supports user contribution

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 tools

Lacks pre-made templates

No version control

Limited model customization

No multi-user collaboration

Reliance on users' own data

No clear support channels

Insufficient documentation

No SDKs for development

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