ML Observability Checklist for Startups

Startups are crucibles for innovation, but getting from zero to one is difficult — particularly for machine learning (ML) teams. In a company’s earliest stages, ML teams must not only prove the value of ML internally but also train, deploy, and maintain a company’s first models. That means doing more with less. It also means laying a good technical foundation for future success without significantly adding to overhead. Based on Arize’s experience tracking billions of model predictions daily for top startups, this guide on what to look for in an ML observability platform covers:

• Model Lineage, Validation & Comparison
• Unstructured Data Monitoring
• Data Quality & Drift Monitoring & Troubleshooting
• Performance Monitoring & Troubleshooting
• Explainability
• Business Impact Analysis
• Integration Functionality
• UI/UX Experience & Scalability To Meet Current Analytics Complexity