Algorithm Container Framework for Dynamic Retail ML Pipeline Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing retail technologies lack an automatic method to select the best algorithms for machine learning pipelines based on dynamic business scenarios, often requiring manual interventions and failing to consider both analytical and business criteria.
Innovation Solution
A method and system that utilize an algorithm container framework to automatically identify the best fit algorithm for retail business problems by processing historical data, generating statistical features, and fine-tuning parameters and hyperparameters using an algorithm calibrator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual intervention is used to select algorithms, then business criteria can be considered, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-service by automatically selecting and evaluating algorithms without requiring manual intervention from business experts. The algorithm selector component autonomously evaluates multiple algorithms against both analytical and business criteria, eliminating the need for human involvement in the algorithm selection process while maintaining accuracy in meeting business requirements
Solution Approach 2:
The system implements feedback mechanisms where the output of algorithm evaluation is fed back into the selection process. The algorithm calibrator uses feedback from model performance metrics to automatically adjust and fine-tune algorithm parameters, creating a closed-loop system that continuously optimizes algorithm selection based on actual performance data
2Measurement precision
If existing algorithm frameworks are used, then analytical criteria can be evaluated, but business criteria are completely missed
Solution Approach 1:
The algorithm container framework provides universality by accommodating both analytical and business criteria within a single unified system. The framework can evaluate algorithms using multiple dimensions including analytical metrics (accuracy, precision) and business criteria (relevance to business rules, domain-specific ranges), making it universally applicable to various retail business problems without requiring separate evaluation systems
Solution Approach 2:
The system introduces an intermediary layer in the form of an algorithm selector and calibrator that mediates between analytical evaluation and business requirements. This intermediary component translates business criteria into actionable evaluation parameters and bridges the gap between technical algorithm performance and business applicability, ensuring both analytical and business perspectives are integrated
3Reliability
If multiple algorithms are evaluated, then best algorithm can be identified, but system complexity and processing requirements increase
Solution Approach 1:
The system applies segmentation by dividing the algorithm evaluation process into distinct modular components: data processing module, feature generation module, algorithm evaluation module, and algorithm calibrator module. Each segment handles specific tasks independently, allowing multiple algorithms to be evaluated through a structured framework that manages complexity through organization and separation of concerns
4Productivity
If automatic algorithm selection is implemented, then development time is reduced, but evaluation metrics and logging options are limited
Solution Approach 1:
The system implements comprehensive feedback mechanisms that capture and log evaluation metrics at multiple stages. The algorithm calibrator continuously monitors performance metrics and logs intermediate results, standards, and outcomes, providing detailed feedback loops that maintain information flow throughout the automatic algorithm selection process, preventing any loss of critical evaluation data
Data Source
Figure 1
Figure 2
Figure 3
AI summary
With changing customer trends and increasing competition, retailers are expected to react quickly and execute their machine learning (ML) pipeline strategies in a very short window and find which algorithm/model works better for their problem statement. Present disclosure provides method and system for dynamic retail requirements. The system uses algorithm container framework that helps in identifying best fit algorithm to solve retail problem. In particular, algorithm container framework finds best hyperparameter and functional parameters of algorithm to attain desired optimal result. The system also uses algorithm calibrator for fine tuning parameters and hyperparameters used for algorithm to achieve optimal result. Once optimal results are obtained, system logs inputs such as best algorithm and their associated optimal results from ML pipeline into algorithm calibrator repository which is then used for fine tuning of stages to obtain accurate results.