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.
| 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 |