Visual Workflow Editor for Machine Learning Model Deployment
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Solution Overview
Problem
Existing machine learning model deployment and workflow generation processes are inefficient and error-prone, requiring manual analysis and data pipeline creation, which is time-consuming and prone to human error, especially for users with limited programming skills.
Innovation Solution
An integrated model execution and deployment system that allows end users to create and revise workflows using a graphical user interface, enabling the integration of machine learning models with additional analytical protocols and logical rules to make informed decisions, without requiring extensive programming knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual analysis and data pipeline creation are used for machine learning model deployment, then users can generate workflows, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs preliminary actions by automatically generating workflow code and data pipelines before user execution. The server pre-compiles the visual workflow into executable code, pre-validates the pipeline configuration, and pre-sets up the data processing architecture, eliminating the need for users to manually create these elements during runtime.
Solution Approach 2:
The system enables self-service by allowing users to create workflows through visual interface interactions without requiring programming knowledge. The platform automatically handles code generation, pipeline compilation, and execution setup, making the complex deployment process serve itself through automation rather than requiring expert manual intervention.
2Adaptability or versatility
If manual pipeline creation is used, then workflows can be customized, but human error is introduced
Solution Approach 1:
The system replaces the mechanical manual coding process with an automated code generation mechanism. Instead of users manually writing and debugging pipeline code, the system automatically translates visual workflow definitions into executable code, eliminating human errors in syntax and logic while preserving full customization capabilities through the visual interface.
Solution Approach 2:
The system implements feedback by automatically validating workflow configurations and providing error detection before execution. The server compiles the visual workflow into code and performs validation checks, returning feedback on configuration errors or incompatibilities, allowing users to correct issues before deployment and ensuring reliable, error-free workflows.
3Manufacturing precision
If programming knowledge is required for workflow creation, then precise control is achieved, but accessibility is reduced
Solution Approach 1:
The system introduces an intermediary layer between the user and the complex programming operations. The visual interface serves as a mediator that translates simple drag-and-drop actions into precise code generation and pipeline configuration, allowing users without programming knowledge to achieve the same level of control and precision as expert developers.
Solution Approach 2:
The system uses copying by providing pre-built workflow templates and components that users can replicate and modify. Instead of requiring users to program from scratch, the platform offers reusable pipeline patterns and model configurations that can be copied and adapted, maintaining precision while dramatically reducing the skill barrier and improving accessibility.
Data Source
AI summary
Disclosed herein are methods and systems to generate and revise a workflow that utilizes machine learning model nodes and other analytical nodes to analyze data and generate a decision via allowing a user to interact with input elements of a graphical user interface. The methods and systems use a processor to provide, for rendering by a user device, a graphical user interface comprising at least a first graphical indicator corresponding to a computer model node within workflow code and a second graphical indicator corresponding to a decision node within the workflow code, the computer model node visually connected with the decision node; and in response to receiving, via a user interacting with the graphical user interface, an additional node corresponding to at least one analytical protocol, revise the workflow code, by adding the analytical protocol before an execution of the decision node.