AI Production Pipeline Configuration for Compliant Model Deployment
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Solution Overview
Problem
The integration of AI models from development to production environments is hindered by differences in technical environments and skillsets, leading to incompatibility issues and requiring substantial engineering efforts due to non-standardized processes and differing data sources.
Innovation Solution
A system that automatically deploys AI pipelines in production environments by wrapping AI models with standardized interfaces, integrating utility components, and generating sequences based on configuration files, ensuring compatibility and compliance with regulatory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a specialized production pipeline is built on a use-case by use-case basis to integrate AI models from development to production environment, then compatibility and integration issues are resolved, but engineering efforts and time consumption increase substantially
Solution Approach 1:
The patent creates a universal production pipeline framework that can handle multiple AI models and use cases through standardized interfaces and configuration files. The system defines a common structure with standardized components (data loaders, preprocessors, model loaders, postprocessors) that can be configured differently for each use case without requiring custom pipeline building, thus achieving both compatibility and efficiency
Solution Approach 2:
The patent uses configuration files with parameters to define different pipeline behaviors for different use cases. By changing parameters in the configuration (model paths, data sources, component settings) rather than changing the pipeline structure itself, the system can adapt to different requirements efficiently, resolving the contradiction between customization and time consumption
2Adaptability or versatility
If non-standardized processes are used during model development to accommodate different data sources and frameworks, then flexibility and adaptability are improved, but integration into production environment becomes challenging
Solution Approach 1:
The patent introduces standardized interfaces and a configuration file as intermediaries between the diverse development environment and the production pipeline. The configuration file acts as a mediator that translates various development setups into a unified production format, while standardized interfaces mediate the interaction between different components, thus maintaining development flexibility while simplifying integration
Solution Approach 2:
The patent segments the production pipeline into independent, standardized components (data loading, preprocessing, model loading, inference, postprocessing) that can be configured and assembled differently for each use case. This segmentation allows each component to maintain its own flexibility while the overall system achieves integration through standardized connections between segments
Data Source
AI summary
An example operation may include one or more of storing utility components for a production environment within a storage, receiving, via a software application, an AI model from a development environment, receiving a configuration file defining a configuration of a pipeline in the production environment which includes the AI model, generating, via the software application, a production pipeline of the AI model which includes a sequence of components including the AI model and at least one utility component based on the configuration file, determining whether the production pipeline satisfies at least one regulatory requirement based on the sequence of components, in response to the production pipeline satisfying the at least one regulatory requirement, executing the production pipeline on input data via the software application in the production environment and may further include an AI agent updating the configuration file of the production pipeline satisfying the at least one regulatory requirement.