AI Pipeline Orchestration With Wrapped Models and Utility Components
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Solution Overview
Problem
The difference between technical environments and skillsets used during model development and deployment creates a bottleneck for integrating models from development to production, often requiring substantial engineering efforts and time due to non-standardized processes and incompatible data sources.
Innovation Solution
A system that automatically deploys AI pipelines in production environments by wrapping AI models with standardized interfaces, using a configuration file to sequence utility components like HTTP parsers and data validators, and ensuring compliance with regulatory requirements through centralized logging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If specialized production pipelines are built on a use-case by use-case basis to integrate model developer pipelines, then integration compatibility between development and production environments is improved, but engineering efforts and time consumption increase substantially
Solution Approach 1:
The patent creates a universal production pipeline framework that can handle multiple use cases through standardized components. The framework defines common interfaces and data schemas that work across different AI model types and deployment scenarios, eliminating the need to build specialized pipelines for each use case while maintaining integration compatibility.
Solution Approach 2:
The production pipeline is divided into modular, reusable components with well-defined interfaces. Each component performs a specific function (data ingestion, model inference, output processing) and can be independently configured and deployed. This segmentation allows rapid assembly of pipelines for different use cases without rebuilding entire systems.
2Adaptability or versatility
If non-standardized processes are used during model development to accommodate diverse data sources and skillsets, then developer flexibility and adaptability are improved, but integration difficulty into production environment increases
Solution Approach 1:
The patent introduces standardized interface layers and adapter components that act as intermediaries between diverse development environments and the production pipeline. These intermediaries translate various data formats and process conventions into a unified production-ready structure, allowing developers to maintain flexibility while ensuring smooth integration.
Solution Approach 2:
The framework allows configuration of pipeline parameters through standardized metadata and configuration files. Developers can specify data sources, model parameters, and processing options without changing the core pipeline structure. This parameter-based configuration maintains developer flexibility while simplifying integration through consistent interfaces.
Data Source
AI summary
An example operation may include one or more of storing utility components within a storage of a software application, receiving a configuration file defining component configurations, receiving an artificial intelligence (AI) model which comprises at least one of source code, binary code, software end points, and configuration information, wrapping the AI model into a wrapped AI model that includes an interface that provides access to the AI model, generating a production pipeline for the AI model which includes a sequence of components including the wrapped AI model and at least one utility component connected to the interface based on the component configurations included in the configuration file, and executing the production pipeline for the AI model on input data via the software application to generate an inference result. The example operation may further include an AI agent modifying the configuration file for the production pipeline to generate the inference result.