AI Risk Scoring for Cloud Resource Optimization Recommendations

An AI-based risk assessment service acts as an intermediary between the optimization recommendation system and human users. This service automatically evaluates risks associated with computing resource optimization recommendations using machine learning models, providing risk scores and assessments that assist human decision-makers without requiring their direct involvement in the technical assessment process.

The patent replaces manual mechanical assessment processes with an automated AI-based system. The mechanical system of human experts manually reviewing recommendations is substituted with an automated risk assessment service that uses machine learning models to evaluate risks, calculate risk scores, and generate assessments automatically, thereby eliminating labor and time costs associated with manual assessment.

The risk assessment system is segmented into distinct functional components: a risk assessment service layer that handles high-level coordination, machine learning models that perform specific risk evaluation tasks, and integration interfaces that connect to cloud service providers. This segmentation allows each component to be optimized independently and simplifies the overall system architecture.

The AI-based risk assessment service serves as an intermediary layer between the complex machine learning models and the users, abstracting away the complexity of the underlying models. Users interact with simple risk scores and assessments rather than dealing with the complex mechanics of the machine learning evaluation process.

The risk assessment service acts as an intermediary that manages the complexity of sophisticated machine learning models. It provides a simplified interface to users while handling complex model training, evaluation, and inference processes in the background, thereby enabling high productivity without exposing users to model complexity.

The system uses machine learning models that are trained on historical data and can be replicated and deployed across multiple environments. Once a model is trained and validated, it can be copied and used to assess risks for different cloud service providers and different optimization scenarios, maintaining high productivity while managing model complexity through reuse.