Data Pipeline Tool for ML Model Development
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine-learning (ML) model development processes are hindered by the time-consuming task of identifying, acquiring, sorting, and filtering data from various sources, which often requires writing API requests, data translation, and appropriate storage for model development, training, testing, and deployment.
Innovation Solution
A data pipeline tool provides a graphical user interface (GUI) for designing and configuring data pipelines or workflows that define how ML models are developed, trained, tested, validated, or deployed, enabling users to interact with predictive results and share them across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data acquisition and processing methods are used for ML model development, then data can be obtained from multiple sources with different formats and protocols, but the process becomes extremely time-consuming and complex
Solution Approach 1:
The patent introduces a data pipeline tool as an intermediary system that mediates between multiple data sources and the ML model development process. This tool provides a unified interface and automated workflows that connect to various data sources through different protocols and APIs, eliminating the need for programmers to manually write API requests for each source. The intermediary handles data acquisition, translation, and preprocessing automatically, thus maintaining versatility in data source access while dramatically reducing the time and effort required.
2Manufacturing precision
If programmers manually write API requests and translate data for model development, then data can be processed according to specific requirements, but the complexity and time investment increase significantly
Solution Approach 1:
The data pipeline tool implements self-service capabilities by automatically performing data acquisition, translation, and preprocessing tasks without requiring manual programming. The system autonomously connects to data sources, retrieves data in various formats, translates it into the appropriate format for ML models, and prepares it for training, testing, and deployment. This automation maintains high data processing accuracy while eliminating the complexity of manual configuration and coding.
3Adaptability or versatility
If data is stored in multiple formats and languages across different sources, then data accessibility is improved, but the time required to sort and filter appropriate data increases
Solution Approach 1:
The patent implements a universal data pipeline tool that can handle multiple data formats, languages, and protocols through a single unified interface. The system is designed to work with diverse data sources (databases, files, APIs, cloud services) and automatically adapts to their specific formats. This multi-functional capability maintains broad data compatibility while significantly improving productivity by eliminating the need for separate processing procedures for each data type, thus reducing the time required for sorting and filtering.
Data Source
AI summary
A data pipeline tool provides a machine-learning design interface that a user can utilize (e.g., via an electronic device such as a personal computer, tablet, or smart phone) to design or configure data pipelines or workflows defining the manner in which ML models are developed, trained, tested, validated, or deployed. Once deployed, a designed ML model may generate predictive results based on input data fed to the ML model. The tool may present the predictive results via a GUI, and may enable a user to mark-up or otherwise interact with those predictive results. The tool may enable the user to share the results (which may include a mark-up or annotation provided by a user).