Container-Based AI Platform Installation via Dynamic Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current AI platforms are inefficient due to reliance on cloud-based solutions, which can be costly and insecure, and lack automated methods for identifying and installing necessary containers for local implementation, leading to over-resource usage and data security concerns.
Innovation Solution
A method and system for installing a software package with a container-based architecture that uses a user interface to receive specifications, applies natural language processing to identify the required subset of containers and compute resource needs, and allows for local deployment without relying on cloud technology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud-based platform is used to host comprehensive AI platform, then unlimited resources and platform accessibility are provided, but data security control is lost and cost increases
Solution Approach 1:
The patent segments the AI platform into individual container modules that can be selectively deployed. Instead of requiring a complete cloud-based platform, users can install only specific containers (e.g., Jupyter Notebook, Scikit-Learn, TensorFlow) locally on their devices or private servers, enabling platform accessibility while maintaining data security control through local deployment
Solution Approach 2:
The patent extracts the essential AI functionality from the cloud-based platform by packaging AI tools and libraries into standalone container images. These extracted containers can be downloaded and run locally, removing the dependency on cloud infrastructure while preserving the core AI capabilities needed for data analysis
2Reliability
If entire AI platform is installed locally, then data security control is maintained, but resource requirements and installation complexity increase
Solution Approach 1:
The AI platform is divided into discrete container modules, each providing specific functionality (e.g., data exploration with Jupyter, machine learning with Scikit-Learn, deep learning with TensorFlow). Users can select and install only the containers relevant to their specific needs, reducing installation complexity and resource requirements while maintaining local data security
Solution Approach 2:
The container-based architecture provides universal AI capabilities that can be deployed in multiple environments (local devices, private servers, or cloud). The same container images work across different operating systems and hardware configurations, simplifying installation and deployment while maintaining data security control
3Loss of energy
If cloud-based platform is used, then platform cost is reduced for users, but interconnectivity with local resources deteriorates
Solution Approach 1:
The patent extracts AI functionality into portable container images that can be executed locally on user devices. This eliminates the need for continuous cloud connectivity, reducing platform costs and improving interconnectivity with local resources such as databases, file systems, and hardware accelerators while maintaining AI capabilities
4Adaptability or versatility
If comprehensive AI platform with all containers is deployed, then all AI functionalities are available, but resource usage and installation time increase
Solution Approach 1:
The AI platform is segmented into independent container modules, each providing specific AI functionality. Users can select and install only the containers needed for their specific projects (e.g., only Jupyter and Pandas for data analysis, or only TensorFlow for deep learning), significantly reducing installation time and resource usage while maintaining access to comprehensive AI functionalities when needed
Solution Approach 2:
The container-based platform enables dynamic selection and deployment of AI tools based on specific project requirements. Users can add or remove containers as needed, allowing the system to adapt to changing functionality requirements without requiring complete platform reinstallation, thus improving productivity
Data Source
AI summary
A method (100) for installing a software package (38) on at least one computer (18) in which the software package has a container-based architecture and including a set (32) of containers (34) includes: providing a user interface (UI) (28) via which a use specification (30) is received from a user; identifying a subset of the set of containers based at least in part on comparing the received use specification with descriptors of the containers of the set of containers; computing resource requirements for the containers of the subset; and displaying at least one of (i) a list (36) of the containers of the subset and (ii) the computing resource requirements for the containers of the subset.