Abstract: Disclosed herein are system, method, and computer program product embodiments for providing application resiliency using a machine learning model trained to detect potential failures based on computational transaction metrics. A resiliency system may monitor metrics related to an application executing on an enterprise data system. The resiliency system may apply these metrics to a machine learning model trained to identify a potential application failure based on application usage trends. In response to detecting a potential failure of the application, the resiliency system may instruct the application to execute one or more resiliency actions. These may include one or more circuit breaker, rate limiter, time limiter, and/or bulkhead actions. The resiliency actions may aid the application in avoiding failure states. The resiliency actions may also be modified based on feedback metrics to aid the application in quickly restoring service once the failure state has been avoided.