Continuous monitoring and managing the model availability and performance is the key to the success of your AI journey. To learn more, download our operational AI architecture white paper.
AutoML the model and continuously improve it through regular feedback, re-model with newer data sets, and further hyperparameter fine tuning.
Use microservice architecture, containerization, to bring AI application and pipeline to your existing DevOps pipeline.
Run the model through AI pipeline to DevOps pipeline as a cloud-native application over a highly automated, using AI methodologies - called AIOps, infrastructure.
Implement an automated feedback loop to collect run time stats, model and application defects, and enforces the service level agreements automatically.