Visual Code Generation System for ML Application Deployment

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Solution Overview

Problem

The existing techniques for generating and deploying software applications, particularly those involving machine learning, are complex and require significant technical expertise, making them time-consuming and resource-intensive, and do not efficiently optimize performance for target destinations.

Innovation Solution

A code generation system that provides user-friendly visual building blocks and pre-configured workflows, allowing users to create, test, and deploy customized machine learning applications through an interactive graphical user interface, optimizing the workflow for the target environment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If traditional software application generation methods are used, then the software application can include numerous libraries and modules, but the process is complex and requires significant technical expertise

Engineering Contradiction:

Improveease of software application generationVSAvoidcomplexity of generation process

Core Design Contradiction:

Ease of manufactureVSDevice complexity

Solution Approach 1:

The software application is segmented into reusable components, libraries, and modules that can be independently developed, tested, and deployed. The system provides a component library with pre-built functional blocks that developers can select and combine, breaking down the complex task of building software from scratch into manageable segments.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary automated generation platform that mediates between the developer's high-level specifications and the complex underlying software construction. This intermediary automatically handles the complexity of integrating libraries, modules, and dependencies, translating user-friendly component selections into complete software applications.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If traditional software deployment methods are used, then the software application can be deployed to a target destination, but the process is time-consuming and resource-intensive

Engineering Contradiction:

Improvedeployment speedVSAvoidtime for deployment process

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-building, pre-testing, and pre-optimizing software components and templates before they are needed. Common software patterns, libraries, and deployment configurations are prepared in advance and stored in a component library, so that when deployment is required, the system can quickly assemble and deploy applications without repeating time-consuming preparation steps.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables rapid deployment by dynamically adjusting deployment parameters such as target environment configurations, resource allocation settings, and optimization levels. The automated generation process can modify these parameters based on the specific deployment scenario, allowing the same software application to be efficiently deployed to different target destinations with minimal manual intervention.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If traditional software generation methods are used, then the software application can be created, but it does not efficiently optimize performance for target destinations

Engineering Contradiction:

Improveperformance optimizationVSAvoidease of use

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system incorporates feedback mechanisms that automatically analyze target destination characteristics, resource availability, and performance requirements. This feedback is used to guide the automated generation process in selecting appropriate components, configuring optimal parameters, and adjusting the software architecture to maximize performance for the specific deployment environment without requiring manual optimization expertise.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20240126518A1Systems and methods for facilitating generation and deployment of machine learning software applications

Publication Date: 2024.04.18 OPENTRONS LABWORKS INC

AI summary

Generally described, one or more aspects of the present application relate to improving the process of generating and deploying software applications in a network environment, particularly software applications that incorporate or rely upon machine learning models. More specifically, the present disclosure provides specific user interface features and associated computer-implemented features that may effectively, from a user's perspective, remove most of the complexities associated with writing and deploying code and developing and improving machine learning models. For example, the present disclosure may provide user-friendly visual building blocks that allow users to build and customize machine learning workflows that can then be turned into a full software application and optimized and deployed at target destinations of the users' choice.