7 Considerations Before Pushing Machine Learning Models to Production

39

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.

https://towardsdatascience.com/7-considerations-before-pushing-machine-learning-models-to-production-efab64c4d433

7 Considerations Before Pushing Machine Learning Models to Production
5 — Setup CI-CDs to automate workflows. git push — and wait for the magic. Before you deploy a project to production, you usually want to make sure it builds and deploys correctly on a staging environment while validating unit tests.
towardsdatascience.com

·

Related Articles & Comments

Comments are closed.