Session Outline
Every model development project encompasses various and essentially steps as data (ingestion, legal and quality assurance), modelling (feature engineering, data splits, multivariate selection), implementation, deployment, and monitoring. Inconsistencies and frictions are inevitable when these steps are not standardized, and technologies are not converged. A proper data and modelling pipeline supported by end-to-end engineering looks to address these issues. In this talk at the NDSML Summit 2023, Aydin Senaydin presents how an effective collaboration between engineers and data scientists can result in a strong pipeline that enables a faster roll-out to various portfolios and countries.
Key Takeaways
- Rationale of an end-to-end modelling pipeline
- Creating less OPEX through faster modelling process
- Value created via standardization
- The importance of pipeline maintenance as much as developing it
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