Automated ML Model Generation via Data Curation
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Solution Overview
Problem
Existing machine learning platforms face inefficiencies due to deficient data quality, such as missing fields, inaccuracies, and biases, leading to errors in predictions and requiring trial-and-error selection of algorithms, which exhausts computing resources and prolongs the production cycle for new software functionalities.
Innovation Solution
An automated data processing and machine learning model generation platform that performs data curation, selects suitable machine learning models, and tunes parameters to improve data quality, reduce errors, and streamline the deployment process, using techniques like rule-based pre-processing, clustering, and unsupervised machine learning for data cleaning and model selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial-and-error selection of machine learning algorithms is performed, then model accuracy may be improved, but computing resources are exhausted and production cycle is prolonged
Solution Approach 1:
The system performs preliminary actions by automatically curating data quality before model training, selecting appropriate algorithms based on data characteristics, and pre-configuring hyperparameters. This preliminary preparation eliminates the need for trial-and-error testing during production, directly resolving the contradiction by improving model readiness in advance while accelerating the production cycle.
Solution Approach 2:
The machine learning platform performs self-service by automatically evaluating data quality metrics, selecting suitable algorithms, and optimizing hyperparameters without human intervention. This automation eliminates resource-intensive manual trial-and-error processes while maintaining or improving model accuracy, thereby resolving the contradiction between reliability and productivity.
2Productivity
If data quality is not curated, then processing speed may be maintained, but prediction errors increase due to missing fields, inaccuracies, and biases
Solution Approach 1:
The system performs preliminary data curation actions including detecting missing fields, correcting inaccuracies, and removing biases before model training begins. By addressing data quality issues in advance, the system ensures high prediction accuracy without requiring reprocessing, thus maintaining processing speed while improving reliability.
Solution Approach 2:
The platform implements feedback mechanisms that automatically evaluate data quality metrics and adjust curation processes accordingly. This feedback loop ensures that data quality standards are met before training, preventing prediction errors while maintaining efficient processing through automated quality control rather than manual intervention.
3Reliability
If manual model selection and parameter tuning is performed, then model performance may be optimized, but deployment time increases
Solution Approach 1:
The system performs self-service by automatically selecting appropriate machine learning algorithms based on data characteristics and autonomously tuning hyperparameters using built-in optimization routines. This automation achieves model performance optimization without manual intervention, significantly reducing deployment time while maintaining or improving model quality.
Solution Approach 2:
The platform automatically adjusts and optimizes model parameters and hyperparameters based on data quality metrics and performance requirements. This automated parameter tuning achieves optimal model performance without manual trial-and-error, thereby reducing deployment time while maintaining high model reliability.
Data Source
AI summary
A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.