AI Model Lifecycle Survey

Basics

Better visualization of model performance comparisons

Improved collaboration between ML and product teams

Integration with existing ML infrastructure

Easier access to relevant production data for testing

Automated testing against problematic examples

Faster detection of model issues in production

Debugging model issues found in production

Experimenting with AI in limited use cases

Multiple AI models in production with active iteration

Advanced AI capabilities fully integrated into core products