Techniques for classifying and recommending software workflows

One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to automatically recommend workflows for software-based tasks by performing the steps of:

• applying a first set of commands associated with a target user to a trained machine-learning model to determine a target distribution of weights applied to a set of tasks, wherein the trained machine-learning model maps different sets of commands to different distributions of weights applied to the set of tasks;
• performing one or more processing operations based on at least two different distributions of weights applied to the set of tasks and the target distribution to determine a first training item;
• generating a recommendation that specifies the first training item;
• and
• transmitting the recommendation to a user to assist the user in performing a particular task.

A system, comprising:

• one or more memories storing instructions;
• and
• one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:
• apply a first set of commands associated with a target user to a trained machine-learning model to determine a target distribution of weights applied to a set of tasks, wherein the trained machine-learning model maps different sets of commands to different distributions of weights applied to the set of tasks;
• perform one or more processing operations based on at least two different distributions of weights applied to the set of tasks and the target distribution to determine a first training item;
• generate a recommendation that specifies the first training item;
• and
• transmit the recommendation to a user to assist the user in performing a particular task.