MLflow or Continual? A Comprehensive Review

Open source platform

Experiment tracking feature

Powerful visualization capabilities

Model evaluation

Model registry

Manages end-to-end workflows

Aids in application building

Tracks progress during fine-tuning

Facilitates packaging and deploying models

Secures hosting models at scale

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

Lack of customer support

Complex Configuration

No GUI

No real-time collaboration

Minimum workflow automation

Limited algorithm support

Incomplete documentation

No built-in hyperparameter tuning

Limited integration options

Dependent on Python environment

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