Cloud-Native AI Model Integration Layer
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Solution Overview
Problem
Deploying artificial intelligence/machine learning models as cloud-native web services is complex due to the need for advanced systems and frameworks, requiring software engineers to write specific logic and configuration, and often necessitating vendor-specific libraries or SDKs, which complicates integration and monitoring in production environments.
Innovation Solution
A modular framework that defines model loading and invocation functions, utilizing an integration layer to load and execute AI/ML models as HTTP web services, with optional plugin functions for data transformation and health status monitoring, allowing for a low-code or no-code configuration through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If vendor-specific libraries or SDKs are used for AI/ML model integration, then model integration capability is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent introduces an integration layer as an intermediary component that sits between the AI/ML model and the cloud platform. This integration layer provides standardized interfaces and abstractions, allowing models to be deployed without requiring vendor-specific libraries or SDKs. The integration layer handles the complexity of model serialization, data format conversion, and platform-specific integration details, thereby simplifying the overall system while maintaining integration capability.
2Adaptability or versatility
If vendor-specific libraries or SDKs are used for AI/ML model integration, then model integration capability is improved, but ease of operation deteriorates
Solution Approach 1:
The integration layer implements universal interfaces that can accommodate multiple AI/ML model formats and cloud platform requirements through a single standardized framework. This multi-functional design allows developers to deploy models without learning multiple vendor-specific tools or libraries, significantly improving ease of operation while maintaining broad integration capability.
3Reliability
If advanced systems and frameworks are used for AI/ML integration, then robustness in production environments is improved, but device complexity deteriorates
Solution Approach 1:
The patent segments the complex AI/ML integration system into distinct functional components: the integration layer, the AI/ML model, and the cloud platform interface. This segmentation allows each component to be developed, tested, and maintained independently. The integration layer encapsulates robustness requirements such as error handling, monitoring, and data validation, separating these concerns from the model itself and reducing overall system complexity.
Data Source
AI summary
Systems and methods for deploying artificial intelligence/machine learning models as cloud-native web services are disclosed. A method may include: (1) starting, by an integration layer on a cloud platform, a webserver accepting requests; (2) scanning, by the integration layer, an application environment for a model loading function for an artificial intelligence/machine learning (AI/ML) model and a model invocation function for the AI/ML model; (3) configuring, by the integration layer, the webserver based on information from an AI/ML service; (4) executing, by the integration layer, the model loading function to load a model object; (5) accepting, by the integration layer and the webserver, an incoming AI/ML model invocation request from a client; (6) executing, by the integration layer, the model invocation function with data by causing the AI/ML service to execute the AI/ML model; and (7) returning, by the integration layer, an output of the AI/ML model to the client.