Machine learning model-guided training and development

A method for use by a system including a computing platform having processing hardware and a system memory storing software code and a machine learning (ML) model including a plurality of node weights, the software code providing a user interface (UI), the method comprising:

• identifying, by the software code executed by the processing hardware, a user of the system;
• obtaining, by the software code executed by the processing hardware a user profile of the user;
• obtaining, by the software code executed by the processing hardware, from respective application programming interfaces (APIs) of one or more applications utilized by the user, activity data relating to use of the one or more applications by the user;
• modifying, by the software code executed by the processing hardware and using the user profile and the activity data, one or more of the plurality of node weights of the ML model to provide a tuned ML model configured to implement at least one of a content-based filtering strategy or a collaborative filtering strategy;
• inferring, by the software code executed by the processing hardware and using the tuned ML model, at least one action for advancing a development of the user;
• outputting to the user, by the software code executed by the processing hardware and using the UI, a recommendation identifying the at least one action;
• obtaining, by the software code executed by the processing hardware, from the respective APIs of the one or more applications utilized by the user, additional activity data relating to further use of the one or more applications by the user;
• and
• further modifying, by the software code executed by the processing hardware and using the additional activity data in a closed-loop feedback process in which user behavior drives ongoing modification of model parameters, the tuned ML model to provide a retuned ML model.