Comment Comment by tixocloud We're building a deployment system based on the following principles that we've learned:- Version control of models and datasets- Code, naming conventions and formatting consistency- Testing of models before deployment into production (in most cases, this helps gain credibility across engineering)- Model review process with a senior data scientist- Kubernetes/Docker for deployment serving as an API or running a scheduled job- Model performance monitoring when in production to identify degradation parent tixocloud 7 years ago
- Version control of models and datasets
- Code, naming conventions and formatting consistency
- Testing of models before deployment into production (in most cases, this helps gain credibility across engineering)
- Model review process with a senior data scientist
- Kubernetes/Docker for deployment serving as an API or running a scheduled job
- Model performance monitoring when in production to identify degradation