Dynamically selecting artificial intelligence models and hardware environments to execute tasks

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer system to:

• receive, from a device connected by a network to a content management system, workload data requesting execution of a task using a machine-learning model;
• extract, from the workload data, workload features defining estimated computational requirements for executing the task;
• determine task routing metrics for a plurality of machine-learning models hosted in respective network environments;
• determine a historical quality metric for each machine-learning model of a plurality of machine-learning models based on historical user feedback data about executing tasks using one or more machine-learning models of the plurality of machine-learning models;
• select a primary machine-learning model for executing the task and a fallback machine-learning model from the plurality of machine-learning models for executing the task if the primary machine-learning model is unavailable based on the workload features defining the estimated computational requirements for executing the task and a first historical quality metric for the primary machine-learning model and a second historical quality metric for the fallback machine-learning model;
• and
• based on detecting that the primary machine-learning model is unavailable prior to executing the task, provide the workload data to a computing environment of the fallback machine-learning model for executing the task.

A system comprising:

• at least one processor;
• and
• at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
• receive, from a device connected by a network to a content management system, workload data requesting execution of a task using a machine-learning model;
• extract, from the workload data, workload features defining estimated computational requirements for executing the task;
• determine a historical quality metric for each machine-learning model of a plurality of machine-learning models based on historical user feedback data about executing tasks using one or more machine-learning models of the plurality of machine-learning models;
• select, utilizing a model selection machine-learning model, a primary machine-learning model for executing the task and a fallback machine-learning model for executing the task if the primary machine-learning model is unavailable based on the workload features defining the estimated computational requirements for executing the task and a first historical quality metric for the primary machine-learning model and a second historical quality metric for the fallback machine-learning model;
• and
• based on detecting that the primary machine-learning model is unavailable prior to executing the task, provide the workload data to a computing environment of the fallback machine-learning model for executing the task.