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Bridging the Gap: From Analytical Models to Operational Success [PyCon DE & PyData Berlin 2024]
Learn practical strategies for successfully integrating analytical models into production systems, from interface design to deployment automation and team collaboration.
- Use interfaces and contracts to manage complexity between analytical models and operational systems
- Implement mapping/translation layers between external API schemas and internal model interfaces to allow independent evolution
- Utilize a model ledger to track when different models are active and manage configuration/deployment
- Move work ahead of deployment time through automation to reduce manual steps and errors
- Add strategic friction points in interfaces where needed to maintain common understanding between teams
- Share responsibility for interface maintenance between engineering and data science teams
- Consider batching requests when possible to improve model performance
- Enable A/B testing through model routing based on customer attributes or business rules
- Use versioned interfaces to ensure reproducibility, especially important in regulated environments
- Decouple deployment decisions from model activation timing through configuration