AI Model Lifecycle Orchestration for Local Deployment
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Solution Overview
Problem
Deploying and managing Artificial Intelligence (AI) models at local sites is challenging due to the need for manual configuration, training, and continuous monitoring, which requires skilled data scientists and is not scalable for multiple sites with diverse data environments.
Innovation Solution
A computer-implemented method and system that orchestrates the lifecycle of AI models by selecting a pre-packaged AI model template, instantiating it, training it with local data, deploying it as a service, monitoring for drift, and re-training as needed, using a machine learning pipeline and lifecycle services for caching, versioning, and governance, allowing for easy customization and adaptation to client-specific data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models are manually configured, trained, and monitored at each local site, then the AI model can be adapted to local data sources, but the complexity and time required for deployment increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-configuring AI model templates with all necessary components (model architecture, training code, dependencies) before deployment. This allows the templates to be instantly instantiated at local sites without manual configuration, resolving the contradiction between adaptability and deployment complexity.
Solution Approach 2:
The patent uses copying by creating reusable AI model templates that can be replicated across multiple local sites. Each template contains the complete model definition and can be copied to any site, automatically adapting to local data sources without requiring manual recreation, thus reducing deployment complexity while maintaining adaptability.
2Reliability
If highly skilled data scientists are used to develop and deploy AI models, then the model quality and performance improve, but the barrier to adoption increases
Solution Approach 1:
The patent implements self-service through automated lifecycle management that handles model training, monitoring, and retraining without requiring skilled data scientists at each local site. The system automatically manages the AI model pipeline, allowing organizations to deploy and maintain high-performance models without specialized expertise, thereby reducing the adoption barrier while maintaining reliability.
Solution Approach 2:
The patent applies universality by creating a unified AI model template system that can be deployed across diverse local sites with different data sources. The universal template framework handles various model types and data configurations through a single standardized interface, enabling broad adoption without requiring specialized knowledge for each specific deployment scenario.
3Adaptability or versatility
If AI models are deployed at multiple local sites with diverse data environments, then the versatility and applicability of AI solutions increase, but the manual work for configuration, training, and monitoring multiplies
Solution Approach 1:
The patent applies segmentation by dividing the AI model into separate, independently deployable templates that can be instantiated at multiple local sites. Each template is a self-contained unit that can be independently configured and deployed, allowing parallel deployment across multiple sites without multiplying manual work, thus improving productivity while maintaining multi-site versatility.
Solution Approach 2:
The patent uses an intermediary approach by introducing a centralized template management system that coordinates deployments across multiple local sites. This intermediary automatically handles the replication and configuration of AI model templates, eliminating the need for manual intervention at each site and maintaining high deployment efficiency across diverse environments.
4Reliability
If continuous monitoring and retraining of AI models is performed manually, then the model accuracy is maintained, but the time and resources required increase
Solution Approach 1:
The patent implements continuity of useful action by establishing automated continuous monitoring and retraining pipelines that operate without interruption. The system continuously monitors model performance and automatically retrains models when drift is detected, maintaining high model accuracy without requiring manual intervention, thereby eliminating the time loss associated with manual maintenance while preserving reliability.
Data Source
AI summary
Provided are techniques for deploying AI models at local sites. A selection of an Artificial Intelligence (AI) model template is received at a local site, where the AI model template is created at a remote site and is packaged in a transportable container. The AI model template in the transportable container is retrieved. A lifecycle of an AI model is orchestrated by: instantiating an AI model from the AI model template, retrieving data from one or more local data sources, training the AI model using the data, deploying the AI model as a service, monitoring the AI model for drift, and, in response to identifying drift, re-training the AI model.