No-Code Machine Learning Platform for Automated Model Generation
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Solution Overview
Problem
Large enterprises face inefficiencies in processing and analyzing vast amounts of data across disparate sources, requiring specialized expertise and time-consuming processes to extract insights for strategic planning, with no existing tools allowing non-machine learning experts to access and utilize flexible compute resources for building models.
Innovation Solution
A domain-independent data processing module that integrates disparate data sources, generates models for prediction and inference, and allows users to build models without coding through a graphical user interface, utilizing machine learning and artificial intelligence algorithms to process data and output models for classification, regression, clustering, and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized machine learning experts are used to process and analyze data, then model accuracy and insights are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system enables users to perform machine learning model creation independently through automated code generation. The platform self-generates executable code from user-selected parameters and data sources, eliminating the need for specialized ML experts while maintaining model accuracy through automated backend processing.
Solution Approach 2:
The system pre-configures the machine learning pipeline with templates and automated code structures. By preparing the computational framework in advance and automatically generating code based on user selections, the system eliminates time-consuming manual setup while preserving model quality through pre-validated processing steps.
2Measurement precision
If machine learning experts manually create models, then model quality is improved, but ease of operation deteriorates
Solution Approach 1:
The platform acts as an intermediary between non-expert users and complex machine learning operations. It provides a simplified graphical interface that translates user-friendly selections into sophisticated ML model creation, maintaining high model quality through automated code generation while improving ease of operation by hiding technical complexity.
Solution Approach 2:
The system replaces manual mechanical processes of model creation with automated computational processes. Instead of requiring users to manually code and configure models, the platform automatically generates executable code from user-selected parameters, preserving model quality through algorithmic precision while dramatically improving ease of operation.
3Ease of operation
If non-experts attempt to build models without specialized tools, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system enables non-expert users to independently create precise models through automated code generation. The platform self-generates optimized code from user-selected data sources and parameters, allowing non-experts to achieve high model precision without manual coding while maintaining ease of operation through the simplified interface.
Solution Approach 2:
The system allows users to control model precision through parameter selection rather than code writing. By enabling users to adjust data sources, model types, and processing parameters through the graphical interface, the system maintains manufacturing precision through configurable parameters while preserving ease of operation for non-expert users.
4Adaptability or versatility
If manual data processing and model creation is performed, then adaptability to specific needs is improved, but productivity decreases
Solution Approach 1:
The system pre-configures flexible model templates and automated processing pipelines that can be quickly adapted to specific needs. By preparing the computational framework in advance with configurable parameters, the system enables rapid deployment of customized models without manual coding, improving productivity while maintaining adaptability through parameter adjustment.
Solution Approach 2:
The platform provides a universal interface that handles multiple data sources, model types, and processing requirements through a single system. This multi-functional approach allows users to adapt the system to specific needs by selecting different parameters and data sources while maintaining high productivity through automated processing rather than manual operations.
Data Source
AI summary
Various methods, apparatuses/systems, and media for generating a data model are disclosed. A processor receives data from a plurality of data sources; displays, onto a graphical user interface (GUI), a plurality of selectable icons for receiving user input in selecting a set of attributes data related to generating a desired data model; receives user input of the selected set of attributes data; automatically creates an executable custom code based on the received data from the plurality of data sources and the selected set of attributes data; executes the custom code; calls, in response to executing, a backend platform for processing the received data from the plurality of data sources and the selected set of attributes data; and automatically generates, in response to calling, the desired data model based on the processed received data and the selected set of attributes data.