ML Model Version Control for Retail Demand Forecasting
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Solution Overview
Problem
Existing machine learning models for demand forecasting in retail are often incompatible across different supply chain entities, and updating these models to incorporate new features can impair their functionality and accuracy. Additionally, manual testing of these models is labor-intensive and prone to human errors.
Innovation Solution
A model training system that utilizes a version-control system to modify machine learning models in feature branches, automatically tests these modifications against the original models using historical data, and deploys accurate updates to supply chain entities and planning systems through continuous integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are updated to incorporate new features, then the models can handle more complex forecasting scenarios, but the functionality and accuracy of existing models may be impaired
Solution Approach 1:
The patent divides the model update process into separate feature branches for different supply chain entities. Each branch maintains its own model version, allowing independent updates without affecting other entities. This segmentation enables new features to be tested in isolation before integration, preventing accidental impairment of existing model functionality.
Solution Approach 2:
The system performs automated testing of model updates against historical data before deployment to production systems. This preliminary action validates that new features improve or maintain model accuracy without introducing errors, ensuring reliable functionality is maintained during the update process.
2Measurement precision
If manual testing of machine learning models is performed, then detailed validation can be conducted, but substantial effort and time are required and human errors may occur
Solution Approach 1:
The system implements automated testing that self-validates model predictions against historical data without requiring manual intervention. The automated pipeline independently executes tests, compares predictions against expected outcomes, and reports results, eliminating the need for manual testing effort while maintaining thorough validation.
Solution Approach 2:
The patent replaces manual testing processes with automated computer-based validation systems. The automated system executes statistical tests, compares model outputs against historical truth, and generates validation reports automatically, substituting human labor with computational processes that are both thorough and efficient.
3Productivity
If machine learning models are deployed to production systems, then demand forecasting can be performed, but deployment errors and incompatibility issues may arise
Solution Approach 1:
The system incorporates automated feedback loops that continuously test model predictions against historical data before deployment. This feedback mechanism identifies compatibility issues and accuracy problems early in the development process, allowing corrections to be made before production deployment, thereby ensuring reliable and error-free implementation.
Solution Approach 2:
The patent performs comprehensive automated testing of model compatibility and accuracy against historical data before deployment to production systems. This preliminary validation ensures that models are thoroughly vetted for correctness and compatibility, preventing deployment errors and ensuring smooth transition to production environments.
Data Source
AI summary
A system and method are disclosed to generate, modify, and deploy machine learning models. Embodiments include a database comprising historical sales data and a server comprising a processor and memory. Embodiments receive historical sales data comprising aggregated sales data for one or more items sold in one or more stores over one or more past time periods. Embodiments train a first machine learning model to learn model parameters and generate sales predictions by identifying one or more causal factors that influence the sale of one or more items. Embodiments train a second machine learning model, based on the first machine learning model, to generate second predictions. Embodiments evaluate the predictions of the first and second machine learning models as compared to the historical sales data, and deploy the machine learning model that generated the predictions that are closer to the historical sales data to generate one or more subsequent predictions.