Avoiding failed transactions with artificial-intelligence based monitoring

A system comprising:

• a processor;
• and
• a non-transitory, computer-readable medium comprising instructions that are executable by the processor for causing the processor to:
• receive training data that includes relationships between (i) characteristics of failed requests transmitted through pipelines and (ii) computing services within the pipelines at which the failed requests failed, wherein the pipelines include respective sequences of computing services configured to handle a plurality of different requests;
• train a machine-learning model using the training data to learn relationships between the characteristics of the failed requests and the computing services at which the failed requests failed, thereby generating a trained machine-learning model;
• provide at least one characteristic of a pending request as input to the trained machine-learning model, the trained machine-learning model being configured to generate a failure prediction based on the at least one characteristic of the pending request, the failure prediction indicating that the pending request is likely to fail and an computing service of a pipeline at which the pending request is predicted to fail;
• in response to the failure prediction indicating that the pending request is likely to fail, determine an intervention for the pending request based on the computing service at which the pending request is predicted to fail;
• and
• execute the intervention.

A non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to:

• receive training data that includes relationships between (i) characteristics of failed requests transmitted through pipelines and (ii) computing services within the pipelines at which the failed requests failed, wherein the pipelines include respective sequences of computing services configured to handle a plurality of different requests;
• train a machine-learning model using the training data to learn relationships between the characteristics of the failed requests and the computing services at which the failed requests failed, thereby generating a trained machine-learning model;
• provide at least one characteristic of a pending request as input to the trained machine-learning model, the trained machine-learning model being configured to generate a failure prediction based on the at least one characteristic of the pending request, the failure prediction indicating that the pending request is likely to fail and an computing service of a pipeline at which the pending request is predicted to fail;
• in response to the failure prediction indicating that the pending request is likely to fail, determine an intervention for the pending request based on the computing service at which the pending request is predicted to fail;
• and
• execute the intervention.