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.

Key takeaways
  • 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