Utilizing a combination of machine learning models to determine a success probability for a software product

A device, comprising:

• one or more memories storing a set of instructions;
• and
• one or more processors, coupled to the one or more memories, the one or more processors configured to execute the set of instructions to:
• receive project management data identifying a project management life cycle associated with development of a software product;
• process a first portion of the project management data, with a first plurality of machine learning models, to generate timeliness scores for the software product;
• combine the timeliness scores to determine an overall timeliness score for the software product;
• process a second portion of the project management data, with a second plurality of machine learning models, to generate quality scores for the software product;
• combine the quality scores to determine an overall quality score for the software product;
• process a third portion of the project management data, with a third plurality of machine learning models, to generate product readiness scores for the software product;
• combine the product readiness scores to determine an overall product readiness score for the software product;
• process the overall timeliness score, the overall quality score, and the overall product readiness score, with a fourth machine learning model, to generate a success probability for the software product;
• and
• perform one or more actions based on the success probability for the software product.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive project management data identifying a project management life cycle associated with development of a software product;
• process a first portion of the project management data, with a first plurality of machine learning models, to generate timeliness scores for the software product and an overall timeliness score based on the timeliness scores;
• process a second portion of the project management data, with a second plurality of machine learning models, to generate quality scores for the software product and an overall quality score based on the quality scores;
• process a third portion of the project management data, with a third plurality of machine learning models, to generate product readiness scores for the software product and an overall product readiness score based on the product readiness scores;
• utilize a fourth machine learning model, with the overall timeliness score, the overall quality score, and the overall product readiness score, to generate a success probability for the software product;
• and
• perform one or more actions based on the success probability for the software product.