Self-Service Machine Learning Framework with Drag-and-Drop Interface
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Developing machine learning solutions is a time-consuming and complex process, often requiring significant statistical expertise, with existing frameworks adding complexity and long debugging times, leading to suboptimal code quality and adherence to best practices.
Innovation Solution
A self-service machine learning framework with a drag-and-drop user interface that includes Data Services, Model Services, and Validation Services, allowing for the sequencing and execution of workflows to predict events, thereby reducing development time and improving code quality, while being platform agnostic and extendable to support various use cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing machine learning frameworks are used, then model building capability is provided, but system complexity increases and debugging time extends
Solution Approach 1:
The system enables self-service machine learning workflows where the framework automatically manages data curation, preparation, and model building processes without requiring extensive manual intervention or debugging, thus reducing system complexity while maintaining productivity
Solution Approach 2:
The machine learning process is segmented into distinct services (Data Services, Model Services, Validation Services) that can be independently configured and executed, reducing overall system complexity by breaking down the monolithic framework into manageable modular components
2Adaptability or versatility
If manual coding is used for machine learning solutions, then flexibility is achieved, but development time increases and code quality decreases
Solution Approach 1:
The system uses templates and pre-configured workflows that can be copied and adapted to different machine learning problems, providing flexibility without requiring manual coding from scratch, thus reducing development time while maintaining solution adaptability
3Manufacturing precision
If iterative model building is performed, then model accuracy improves, but overall development time extends
Solution Approach 1:
The system performs preliminary actions by automatically curating and preparing data before model building begins, and by pre-configuring validation workflows, thus enabling iterative model improvement without extending overall development time
4Manufacturing precision
If statistical expertise is required for machine learning development, then solution quality improves, but ease of operation decreases
Solution Approach 1:
The system acts as an intermediary that translates business requirements into machine learning models automatically, eliminating the need for users to have statistical expertise while maintaining solution quality through automated best practice enforcement
Data Source
AI summary
An embodiment of the present invention is directed to reducing complexities in machine learning application development by providing a drag-and-drop user interface for an entire machine learning process. The innovative system significantly reduces development time and efforts. An embodiment of the present invention is directed to applying optimized common components that follow industry wide best practices thereby improving the time to market as well as the overall code quality. The embodiments of the present invention provide adaptability and extendibility to support various platforms. According to an embodiment of the present invention, a generic platform agnostic code generator may be extended to support various use cases, applications, platforms and environments.