The Challenge of ML in Production
Getting a model to work in a notebook is one thing. Getting it to work reliably in production is another challenge entirely.
Key Considerations
- Model versioning and rollback
- A/B testing and canary deployments
- Monitoring and observability
- Cost optimization
Our Recommended Approach
After deploying hundreds of models for enterprise clients, we've developed a battle-tested approach...