Continual or FieldDay? A Comprehensive Review

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

Creates custom vision apps

Uses camera for productivity

Mobile machine learning toolkit

Teaches apps to 'see'

Real-time accuracy visualization

Identifies multiple objects

Label and manage models

Push model iterations

Available for iOS

Comprehensive usage guide

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

Only available on iOS

Potential for privacy concerns

Data collection reliant on users

Limited algorithm refinement control

Requires able smartphone camera

Manual data labelling needed

Real-time accuracy might vary

Single tap might miss-label

No mention of API Integration