Guided ML Model Generation for Faster Predictive Analytics Deployment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The limited availability of data scientists and the long cycle time required to develop machine learning models cause significant reductions in business operation efficiency, particularly in the auto insurance industry, where identifying key features influencing sales based on consumer data correlations is complex and time-consuming.
Innovation Solution
A guided user interface is provided to facilitate non-expert users in constructing machine learning models by obtaining datasets, determining characteristics, selecting algorithms, and training models, which are then implemented in a cloud server for distribution to client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning model development is performed by data scientists, then model accuracy and reliability are improved, but the time required and operational efficiency deteriorate
Solution Approach 1:
The system enables self-service automated model generation where the computer automatically performs model construction, algorithm selection, and training without requiring data scientists. The automated model generation system processes consumer data and generates predictive models autonomously, eliminating the time-consuming manual process while maintaining model quality through systematic automated evaluation and selection procedures.
Solution Approach 2:
The system performs preliminary actions by pre-processing consumer data, pre-selecting relevant features, and pre-configuring multiple machine learning algorithms before the actual model generation. This preliminary preparation enables rapid model creation by having all necessary components ready in advance, significantly reducing the time from data input to model deployment.
2Reliability
If data scientists manually develop machine learning models, then model quality is improved, but productivity and operational efficiency worsen
Solution Approach 1:
The automated model generation system performs self-service by automatically executing the complete model development lifecycle including data processing, algorithm selection, model training, and quality evaluation. This eliminates the need for data scientists to manually perform each step, thereby maintaining model quality through systematic automated procedures while dramatically increasing productivity and operational efficiency.
Solution Approach 2:
The system changes parameters by automatically adjusting model configuration parameters, selecting optimal algorithms based on data characteristics, and tuning hyperparameters through automated evaluation. This parameter optimization maintains high model quality while the automated process enables parallel processing and rapid iteration, significantly improving business operation efficiency.
3Measurement precision
If complex machine learning algorithms are used, then prediction accuracy is improved, but system complexity and ease of operation worsen
Solution Approach 1:
The system handles complexity through self-service automation, where the computer automatically performs complex algorithm selection, parameter tuning, and model training. Users interact with a simplified interface that automatically manages the underlying complexity, enabling non-experts to generate accurate predictive models without needing to understand complex machine learning concepts.
Solution Approach 2:
The system segments the complex model generation process into distinct automated stages: data preprocessing, feature selection, algorithm selection, model training, and evaluation. Each segment is handled automatically by the system, reducing the perceived complexity for users while maintaining high prediction accuracy through systematic processing of each segment.
4Reliability
If specialized data scientists are required for model development, then model reliability is improved, but resource availability and productivity worsen
Solution Approach 1:
The automated model generation system eliminates the dependency on specialized data scientists by performing all model development tasks autonomously. The system maintains model reliability through automated quality checks, performance evaluation, and selection of optimal models, while enabling unlimited parallel model generation that dramatically increases deployment rate and resource utilization.
Solution Approach 2:
The system achieves universality by providing a single automated platform that can generate multiple types of predictive models for various business questions simultaneously. This multi-functional system replaces the need for multiple specialized data scientists with one automated system that handles diverse modeling tasks, improving both reliability through consistent quality standards and productivity through parallel processing capabilities.
Data Source
AI summary
Systems and methods for predictive data analytics are provided. A method comprises generating a guided user interface (GUI) that guides one or more user operations on the user interface including: obtaining, from a database, a dataset including a plurality of data objects; determining one or more characteristics associated with a first data object of the plurality of data objects; identifying a subset of the dataset based at least in part on the one or more characteristics; selecting at least one machine learning algorithm; and training a machine learning (ML) model with respect to the first data object using the subset of the dataset and the at least one machine learning algorithm to generate a trained ML model; implementing the trained ML model with respect to the first data object in a cloud server to enable distributing the trained ML model to a plurality of client device via a network.