According to a recent survey by Arize, nearly one in three (28.6%) ML teams in the financial services industry say it takes them a week more to detect and fix an issue with a model in production. How can fintech companies and others in the industry stay a step ahead? ML observability is a key part of the answer and a critical element for any modern ML stack.
This checklist covers the essential elements to consider when evaluating an ML observability platform. Based on our team’s firsthand experience building ML teams from the ground up and tracking billions of daily predictions, this buyer’s blueprint can help with product and technical requirements to consider across:
- Fintech-Specific Use Cases
- Model Lineage, Validation & Comparison
- Data Quality & Drift Monitoring & Troubleshooting
- Performance Monitoring & Troubleshooting
- Explainability
- Business Impact Analysis
- Integration Functionality
- UI/UX Experience & Scalability To Meet Current Analytics Complexity