ML Operations Framework for Automated Model Deployment
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Solution Overview
Problem
Enterprises face challenges in consistently and efficiently deploying machine learning models across different organizational units due to varying goals, guidelines, and lack of a common toolset, leading to manual hand-offs and technical debt, which hampers real-time model deployment and performance.
Innovation Solution
A machine learning framework that includes an analytics data store, data virtualization, a feature pipeline for model creation, a consumer layer for operational system processing, a real-time inference platform for deployment, and a CI/CD platform for automated feedback, enabling automated and efficient model development, deployment, and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual hand-off processes are used for ML model deployment, then organizational boundaries and departmental strategies can be respected, but deployment efficiency and speed are significantly reduced
Solution Approach 1:
The patent introduces a centralized ML Operations platform as an intermediary between data science teams and operational organizations. This platform automates the hand-off process by providing standardized model artifacts, deployment templates, and monitoring dashboards that operational teams can consume without manual intervention, thus resolving the contradiction between operational flexibility and deployment speed.
Solution Approach 2:
The patent implements automated feedback loops where operational systems provide performance metrics back to the ML Operations platform, which then feeds this information to data science teams for model retraining. This automated feedback mechanism eliminates manual monitoring and reporting, significantly improving deployment speed while maintaining operational flexibility through configurable alerting and notification systems.
2Reliability
If a common toolset and technologies are implemented across organizations, then consistency and stability in the release cycle are improved, but organizational customization and adaptability are reduced
Solution Approach 1:
The patent segments the ML Operations platform into modular, organization-specific instances that can be deployed across different departments. Each organization can configure and customize the platform parameters, data sources, and deployment targets to match their specific needs, while still benefiting from the standardized core architecture. This modular segmentation resolves the contradiction by allowing both consistency at the platform level and customization at the organizational level.
Solution Approach 2:
The patent enables different organizational units to have tailored configurations, data schemas, and deployment strategies while operating within a unified ML Operations framework. Each organization can define local quality parameters such as specific performance thresholds, monitoring metrics, and model validation criteria that are optimized for their unique business requirements, thus maintaining adaptability while achieving overall system consistency.
3Productivity
If automated CI/CD platforms are used for ML model deployment, then deployment efficiency and consistency are improved, but system complexity increases
Solution Approach 1:
The patent designs the ML Operations platform to perform multiple functions within a unified architecture, including model training coordination, deployment automation, monitoring, and feedback collection. By consolidating these functions into a single multi-functional platform rather than separate systems, the patent improves deployment efficiency while managing complexity through functional integration rather than proliferation of independent systems.
Solution Approach 2:
The patent implements self-service capabilities where the CI/CD platform automatically handles model deployment tasks, including environment provisioning, model validation, and rollback operations. The system performs self-diagnosis and self-correction of common deployment issues, reducing the need for manual intervention and simplifying operational procedures despite the underlying complexity of the automated systems.
Data Source
AI summary
According to some embodiments, a ML framework for an enterprise may include an analytics data store containing electronic records that include record characteristics to be processed by a data as a service layer to create data virtualization. A ML feature pipeline may perform feature engineering based on the data virtualization to create a ML model, and a consumer layer may process operational system information utilizing a business rule engine. In addition, a real-time inference platform may expose an API endpoint associated with deployment of the ML model using the data received from the consumer layer. A CI/CD platform may then automatically provide feedback information to the ML feature pipeline regarding performance of the deployed ML model.