Predictive Trait Models With On-Demand Workflow Orchestration
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Solution Overview
Problem
Existing frameworks for customer tracking and profiling are not scalable, flexible, and lack the ability to automatically incorporate on-demand task selection and configuration, leading to inefficient and non-transparent execution of predictive tasks.
Innovation Solution
A novel framework for on-demand creation, evaluation, and deployment of machine learning models that predict user traits, featuring an engagement module, predictive trait UI, predictions service, and orchestrator for automated workflows, enabling customizable and efficient model development with intuitive visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing frameworks for customer tracking and profiling are used, then basic predictive tasks can be performed, but the frameworks are not scalable and lack flexibility for on-demand task selection and configuration
Solution Approach 1:
The system is divided into distinct modular components: a predictive trait UI for user interaction, an orchestrator for workflow management, and a predictions service for model execution. Each module handles specific functions independently, enabling flexible configuration of on-demand predictive tasks while maintaining manageable system complexity through clear separation of concerns.
Solution Approach 2:
The predictive trait system is designed as a universal framework that can handle multiple types of predictive tasks through a common architecture. The orchestrator manages diverse workflows (model training, evaluation, deployment) using standardized processes, and the predictions service can execute different machine learning models for various user trait predictions, providing adaptability without requiring separate specialized systems for each task type.
2Extent of automation
If existing customer profiling frameworks are used, then basic tracking is possible, but automated workflows for model creation, evaluation, and deployment are not provided
Solution Approach 1:
The orchestrator implements feedback mechanisms that track and report the status of automated workflows throughout the model lifecycle. It monitors progress through different stages (training, evaluation, deployment) and provides visibility into system operations through the predictive trait UI, ensuring transparency while maintaining automation efficiency.
Solution Approach 2:
The orchestrator serves as an intermediary layer between the user interface and the predictions service, managing the automated workflows for model creation, evaluation, and deployment. It coordinates tasks across different components, automating complex processes while maintaining observability through standardized interfaces and status reporting that preserve execution transparency.
3Productivity
If machine learning models are developed manually, then customization is possible, but the process is inefficient and not scalable
Solution Approach 1:
The system performs preliminary actions by pre-configuring the framework structure, data pipelines, and model deployment infrastructure in advance. The orchestrator is pre-programmed with workflow templates for common predictive tasks, and the predictions service is pre-established to handle model execution. This preliminary setup enables rapid, efficient model development while maintaining flexibility for customization through configurable parameters and on-demand task selection.
Data Source
AI summary
Systems and methods for on-demand creation, evaluation and/or deployment of machine learning (ML) models for computing the value of a predictive trait. System operations include detecting, at a predictive trait user interface (UI), a selection of a predictive trait of a plurality of predictive traits and a selection of a configuration setting for the predictive trait. System operations further include executing, using an orchestrator, an onboarding flow that retrieves user data, a training workflow that generates a trained predictive trait model, and/or an inference workflow that runs the trained predictive trait model to compute predictive trait values for users in a test set. System operations further include generating and displaying, via the predictive trait UI, explanations associated with the trained predictive trait model, the computed predictive trait values, and the test set. The system further transmits computed predictive values to an audience management service for audience generation or user profiling.