Intelligent task scheduling

A method, comprising:

• training, via at least one computing device, at least one machine-learning model to generate predictive outcomes based on a training data set comprising historical data for a plurality of completed deliverables;
• determining, via the at least one computing device, a plurality of input variables associated with the at least one machine-learning model;
• identifying, via the at least one computing device, a deliverable corresponding to a particular program, the deliverable comprising a plurality of tasks and metadata associated with the deliverable;
• determining, via the at least one computing device, a subset of resources from a plurality of resources, wherein the subset of resources are assigned to the deliverable;
• determining, via the at least one computing device, a predicted completion date for the deliverable by processing, via the at least one machine-learning model, the metadata associated with the deliverable and performance data corresponding to the subset of resources assigned to the deliverable;
• performing, via the at least one computing device, a comparison of the predicted completion date of the deliverable to an expected completion date of the deliverable;
• and
• generating, via the at least one computing device, a recommended modification to the subset of resources assigned to the deliverable based on the comparison.

A non-transitory computer-readable medium embodying a program that, when executed by at least one computing device, causes the at least one computing device to:

• train at least one machine-learning model to generate predictive outcomes based on a training data set comprising historical data for a plurality of completed deliverables;
• determine a plurality of input variables associated with the at least one machine-learning model;
• identify a deliverable corresponding to a particular program, the deliverable comprising a plurality of tasks and metadata associated with the deliverable;
• determine a subset of resources from a plurality of resources, wherein the subset of resources are assigned to the deliverable;
• determine a predicted completion date for the deliverable by processing, via the at least one machine-learning model, the metadata associated with the deliverable and performance data corresponding to the subset of resources assigned to the deliverable;
• perform a comparison of the predicted completion date of the deliverable to an expected completion date of the deliverable;
• and
• generate a recommended modification to the subset of resources assigned to the deliverable based on the comparison.