1. An application failure prediction system comprising: a processor; and a non-transitory data storage comprising machine-readable instructions that cause the processor to: receive for a time interval, real-time data associated with a first application executing on a computing apparatus; analyze the real-time data using a first predictive data model to identify a plurality of anomalies within the real-time data, each of the plurality of anomalies represents a corresponding potential application failure; determine respective anomaly scores for the plurality of anomalies based on the analysis; compare each of the respective anomaly scores with a threshold predetermined within the first predictive data model; indicate high probabilities for occurrence of the corresponding potential application failures for high-scoring anomalies of the plurality of anomalies, the high-scoring anomalies having the respective anomaly scores greater than the threshold; and implement a first set of corrective actions for the high-scoring anomalies, wherein the instructions to implement the first set of corrective actions further comprise instructions that cause the processor to: obtain from data logs of the first application, patterns of error codes similar to error codes corresponding to the high-scoring anomalies, the patterns of error codes corresponding to sequences of error events that occurred during the execution of the first application; determine if one or more of the patterns of error codes are comprised in the data logs of the first application; identify, in response to determining that the one or more of the patterns of error codes are comprised in the data logs, the first set of corrective actions from the data logs as being previously implemented in response to the one or more patterns of error codes; and automatically implement the first set of corrective actions in response to identifying the one or more patterns of error codes in the data logs.