Web-Based AI Model Deployment via Containerized Validation
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Solution Overview
Problem
Data scientists face challenges in operationalizing and sharing AI models due to security risks and the need for manual processes to convert prototypes into cloud-compatible standalone scripts, as well as maintaining compatibility across different environments.
Innovation Solution
The use of container-based AI models validated for security and functionality, allowing data scientists to upload and modify AI models through web-based user interfaces, ensuring secure and compatible deployment across cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data scientists manually convert AI models from local prototypes to cloud-compatible standalone scripts, then the models can be deployed to cloud environments, but the process is time-intensive and complex
Solution Approach 1:
The system segments the model deployment process by using Docker containers to encapsulate individual AI models with their dependencies, separating them into independent, portable units that can be easily transferred and executed in cloud environments without manual conversion
Solution Approach 2:
The patent introduces an intermediary validation service that automatically checks and validates AI model containers before deployment, serving as a mediator between the local development environment and cloud deployment, eliminating the need for manual script conversion
2Ease of operation
If AI models are shared publicly through web-based interfaces, then broader audience access is enabled, but security risks increase from unauthorized actions
Solution Approach 1:
The system performs preliminary validation of AI model containers before making them publicly accessible, checking for security vulnerabilities and proper configuration in advance, so that only validated models are deployed to the web interface, preventing security risks before they can affect the system
Solution Approach 2:
The patent introduces a validation service as an intermediary layer between the public web interface and the AI model execution environment, which filters and validates incoming requests and model configurations, blocking unauthorized actions before they reach the models
3Ease of operation
If data scientists modify AI model code through web interfaces, then ease of modification is improved, but maintaining functionality after reformatting becomes difficult
Solution Approach 1:
The system creates and validates a copy of the AI model in container format before deployment, allowing users to modify the original code through web interfaces while the validated container copy ensures functionality is preserved, separating modification operations from execution requirements
Data Source
AI summary
Systems and methods are described for operationalizing AI models using web-based user interfaces. The system may receive a first user input uploading of a software container, wherein the software container comprises a data model written in a first programming language. The system may generate for display, in a user interface of a web application, a prediction based on the data model, wherein the web application is written in a second programming language, and wherein the web application is configured to receive selection of the parameter and execute, in the software container, the data model.