Automated ML Tractability Evaluation System
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Solution Overview
Problem
The current evaluation process for determining whether a technology problem can be solved using machine learning is excessively manual and time-consuming, leading to inefficiencies and potential delays or non-compliance issues.
Innovation Solution
A computer-implemented method and system that uses a trained tractability machine learning model to automatically evaluate the potential for implementing machine learning on a use case, generating an onboarding machine learning model and automating the onboarding process, reducing human intervention and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation by subject matter experts is used, then accuracy of assessment is improved, but time consumption increases
Solution Approach 1:
An automated evaluation system acts as an intermediary between the use case data and subject matter experts. The system pre-processes and evaluates use case data using machine learning algorithms, providing structured assessments that experts can then review. This intermediary layer filters and prepares information before expert review, maintaining assessment accuracy while significantly reducing the time experts need to spend on initial evaluations.
Solution Approach 2:
The system performs preliminary evaluation of use case data before it reaches subject matter experts. Automated algorithms conduct initial assessments, data validation, and feasibility analysis in advance. This preliminary action handles routine evaluation tasks, allowing experts to focus only on complex cases requiring human judgment, thereby reducing overall time consumption while preserving assessment quality.
2Reliability
If manual review process is used, then quality of evaluation is improved, but productivity decreases
Solution Approach 1:
The evaluation process is segmented into multiple stages: automated preliminary evaluation, intermediate validation, and expert review. Each segment handles specific tasks with appropriate complexity. This segmentation allows routine evaluations to be processed automatically at high speed while reserving expert resources for complex cases, thereby increasing overall productivity without compromising quality.
Solution Approach 2:
The automated evaluation system provides universal functionality for assessing diverse use cases across different domains. A single multi-functional platform handles various evaluation criteria and data types, replacing the need for multiple specialized manual review processes. This universality increases evaluation throughput while maintaining consistent quality standards across all assessments.
3Reliability
If custom solutions are designed by experts, then problem-solving effectiveness is improved, but resource consumption increases
Solution Approach 1:
The system creates standardized evaluation templates and assessment frameworks that can be copied and applied across multiple use cases. Instead of designing custom evaluation approaches for each case, proven templates are replicated and adapted as needed. This copying approach reduces resource consumption by eliminating redundant design work while maintaining problem-solving effectiveness through proven methodologies.
Solution Approach 2:
The system adjusts evaluation parameters and criteria based on the specific characteristics of each use case rather than applying fixed custom solutions. By dynamically changing parameters such as evaluation thresholds, data requirements, and assessment weights, the system efficiently adapts to different problems without requiring extensive custom resource allocation, thereby reducing overall resource consumption.
Data Source
AI summary
Systems and methods for improving an onboarding process of a use case received from a user device using machine learning by automatically evaluating a potential to implement machine learning on the use case and automating the onboarding process, which may include receiving and processing an initial use case data set using a trained tractability machine learning model to generate a first determination whether the use case is machine learning tractable. The trained tractability machine learning model is trained using historical tractability data. Generating an onboarding machine learning model for solving the use case based at least upon the first determination the use case is machine learning tractable. Receiving a feedback data set and processing the initial use case data set and the feedback data set using the trained onboarding machine learning model.