7 Considerations Before Pushing Machine Learning Models to Production

Being part of a company that values scalability, I daily see, as a data scientist, the challenges that come with putting AI-based solutions in production. These challenges are numerous and cover a variety of aspects: modeling and system design, data engineering, resource management, SLA, etc. I don't pretend mastery in any of those fields. I do however know that implementing some software engineering principles and using the right tools helped me a lot in making my work reproducible and ready for production. In this article, I'll share with you 7 of the considerations I have in mind before productionizing my models.