Considerations for deploying generative AI applications in production
Deploying generative AI applications in production environments introduces a new set of challenges that go beyond the considerations for traditional software and ML deployments. While aspects such as functional correctness, system/application security, security scan of artifacts such as model files and code, infrastructure scalability, documentation, and operational readiness (e.g., observability, change management, incident management, and audit) remain essential, there are additional factors to consider when deploying generative AI models.
The following are some of the key additional considerations when deciding on the production deployment of generative AI applications.
Model readiness
When deciding whether a generative AI model is ready for production deployment, the focus should be on its accuracy for the target use cases. These models can solve a wide range of problems, but attempting to test...
