Automated AI Model Serving System for Rapid Adaptation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current AI model development processes lack automation, efficiency, and flexibility in creation, testing, training, and deployment, often requiring manual intervention and specialized environments, which hinders rapid adaptation and deployment.

Innovation Solution

An AI model serving system with automated components for creation, testing, training, and deployment, utilizing base classes, version control repositories, model APIs, and offline model runners to streamline the process, enabling data scientists and development teams to control training and deployment speed, and facilitating model versioning and caching for efficient adaptation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If manual processes are used for AI model creation, testing, training, and deployment, then flexibility and control are maintained, but automation and efficiency are reduced

Engineering Contradiction:

Improveautomation of model creation, testing, training, and deploymentVSAvoidcomplexity of automated system

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The system segments the AI model development process into distinct modular components: model creation module, testing module, training module, and deployment module. Each module handles a specific phase of the workflow, enabling automated processing while maintaining manageable system complexity through functional decomposition.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The automated system is designed as a universal platform that can handle multiple AI model types and development tasks through a single integrated architecture. The system provides multi-functional capabilities including creation, testing, training, and deployment across different environments, reducing the need for separate manual processes for each task.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Productivity

If rapid adaptation and deployment of AI models is pursued, then productivity increases, but testing and validation time may be compromised

Engineering Contradiction:

Improvespeed of model adaptation and deploymentVSAvoidmodel performance and validation

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary testing and validation actions during the model creation phase using offline model runners and test datasets. By conducting validation tests before full deployment, the system ensures model reliability is established in advance, enabling rapid deployment without compromising quality assurance.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The automated system maintains continuous validation and monitoring of model performance after deployment. Testing is not a one-time event but an ongoing process that continues throughout the model lifecycle, ensuring sustained reliability while enabling continuous rapid adaptation and deployment cycles.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS11675581B1Automated creation, testing, training, adaptation and deployment of new artificial intelligence (AI) models

Publication Date: 2023.06.13 DIALPAD INC

AI summary

Functionality is provided for the automated creation, testing, training, adaptation and deployment of AI models and changes thereto. Base classes are provided that enable practicable creation of new models from existing one. New models are tested on live data sets offline from user sites. New training methods are provided for the production of particular outcomes. Efficient adaptation of new AI models is facilitated, encompassing data scientist and development team control over how fast to train and deploy new models.