Automated Forecasting Model Lifecycle Management
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Solution Overview
Problem
Existing forecasting models struggle to maintain prediction accuracy due to rapidly changing business process-related data trends, leading to financial losses as they require manual intervention for model selection and deployment.
Innovation Solution
An end-to-end system that automatically trains, validates, selects, and deploys forecasting models using edge computing resources for real-time data streaming and central computing resources for model training and validation, responding to detected data drift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If forecasting models are manually selected and deployed by data scientists, then model accuracy can be maintained through expert judgment, but the system responds slowly to rapidly changing data trends causing financial losses
Solution Approach 1:
The system enables automated model training, validation, and deployment without requiring manual data scientist intervention. The automated system continuously monitors data trends, triggers retraining when drift is detected, validates new models, and deploys them automatically, allowing the forecasting system to serve itself and respond rapidly to changing conditions while maintaining accuracy through systematic validation processes
Solution Approach 2:
The system performs preliminary actions by continuously training multiple candidate models in advance and maintaining a model registry with pre-validated models. When data drift is detected, the system can quickly deploy from the registry or initiate rapid retraining, rather than starting from scratch. This preliminary preparation significantly reduces response time while ensuring only validated models are deployed
2Productivity
If automated systems are implemented for model training and deployment, then response speed to data trends improves, but system complexity increases requiring infrastructure for automated training, validation, and deployment
Solution Approach 1:
The system divides the automated forecasting pipeline into distinct modular components: data monitoring module, drift detection module, model training module, validation module, and deployment module. Each component has a specific function and can be independently configured and maintained. This segmentation reduces complexity by making the system more manageable and easier to implement incrementally while still achieving high automation levels
Solution Approach 2:
The system implements a universal model registry that can store and manage multiple types of forecasting models (e.g., ARIMA, Prophet, neural networks) using a common validation and deployment framework. The validation pipeline is designed to work with different model architectures and data types, reducing the need for separate infrastructure for each model type and simplifying the overall system while maintaining high productivity
3Reliability
If manual model validation and selection processes are used, then model quality can be ensured through expert review, but the process is prolonged and fails to keep pace with rapidly changing data trends
Solution Approach 1:
The system implements continuous automated validation that runs alongside model training and deployment operations. Multiple validation checks (performance metrics, drift detection, anomaly analysis) are performed continuously rather than in discrete manual steps. This continuous automated validation maintains model quality standards while reducing the duration from weeks/months of manual review to minutes or hours of automated processing
Solution Approach 2:
The system incorporates automated feedback loops where model performance is continuously monitored against validation criteria, and results are fed back to trigger retraining or model selection automatically. Performance metrics and validation results provide feedback that guides the automated decision-making process, ensuring model quality is maintained through systematic feedback-driven adjustments rather than prolonged manual review cycles
Data Source
AI summary
Examples of the presently disclosed technology provide end-to-end systems for automatically training, validating, selecting, and deploying forecasting models in response to fast changing data trends. Such an end-to-end system includes edge computing resources that stream data directly from customer data sources and detect drift between predictions of forecasting models deployed at the edge computing resources and corresponding time-series data derived from the streamed data. The end-to-end system also includes a central computing resource (e.g., a centralized, cloud-based computer cluster) that responds to the drift detections by automatically training, and validating instances of stored forecasting models using fresh time-series data derived from the streamed data. The fresh time-series data may be logically grouped into subsets of time-series data metrics—where each subset of time-series data metrics is associated with a common customer sub-system.