Guided Machine Learning Model Generation for Faster Predictive Analytics
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Solution Overview
Problem
The limited availability of data scientists and the long cycle time required to develop machine learning models for predictive data analytics in consumer industries, particularly in auto insurance, lead to inefficiencies in business operations.
Innovation Solution
A guided user interface (GUI) is used to facilitate non-expert users in constructing machine learning models by obtaining datasets, 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 non-expert users to autonomously build and deploy machine learning models through a guided interface that automatically performs data processing, feature engineering, and model selection. The platform serves itself by providing automated model generation capabilities that eliminate the need for specialized data scientists, thereby reducing development time while maintaining model quality through systematic automated procedures.
Solution Approach 2:
An automated model generation platform acts as an intermediary between business users and machine learning model deployment. This intermediary system handles the complex technical processes of data preprocessing, feature selection, and model training, translating user requirements into functional models without requiring direct intervention from data scientists, thus accelerating the development process while preserving model reliability.
2Reliability
If specialized data scientists are used to develop machine learning models, then model quality is improved, but the availability and scalability of model development deteriorate
Solution Approach 1:
The platform empowers any user with domain knowledge to become a model developer through automated guidance systems. Non-expert users can independently complete the entire model development process from data upload to deployment, eliminating the bottleneck of limited data scientist availability and enabling scalable model development across multiple business units and use cases.
Solution Approach 2:
The automated model generation platform provides universal access to machine learning capabilities for all user types, from business analysts to technical users. The system handles multiple functions including data preprocessing, feature engineering, model selection, training, and deployment through a single unified interface, making model development accessible and scalable across the organization without requiring specialized expertise.
3Manufacturing precision
If manual machine learning model development processes are used, then model customization and precision are improved, but operational efficiency and automation level deteriorate
Solution Approach 1:
The system dynamically adapts the model development process based on user inputs and data characteristics. The automated platform adjusts feature engineering strategies, selects appropriate algorithms, and optimizes hyperparameters in real-time during the training process, maintaining high customization precision while operating at automated speeds. The interface dynamically guides users through relevant decisions without requiring manual execution of each step.
Solution Approach 2:
The platform performs preliminary automated actions including data cleaning, feature extraction, and initial model configuration before user intervention is needed. By pre-processing data and preparing model frameworks in advance, the system enables users to focus on high-level customization decisions while routine precision-critical tasks are already optimized, thereby improving both customization precision and deployment efficiency simultaneously.
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.