Automated ML Pipeline Generation System for Model Exploration
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Solution Overview
Problem
Organizations face difficulties in implementing effective machine learning solutions due to the complexity of algorithmic and mathematical aspects, requiring specialized expertise, and the need for extensive time and resources to develop and deploy machine learning models, which are often specific to particular use cases and environments.
Innovation Solution
An automated machine learning pipeline generation system (AMPGS) that allows users to construct optimized ML pipelines by providing a dataset and setting an exploration budget, automatically exploring combinations of data preprocessors, algorithms, and algorithm parameter settings to find a 'best' model, providing visibility into the model details and allowing for iterative fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated pipeline exploration is implemented, then ease of operation improves, but device complexity increases
Solution Approach 1:
The patent introduces an automated machine learning pipeline generation system that acts as an intermediary between users and complex ML algorithms. This system automatically explores pipeline plans, selects algorithms, tunes hyperparameters, and preprocesses data, shielding users from technical complexity while delivering operational simplicity.
Solution Approach 2:
The system performs self-service by automatically executing pipeline exploration, algorithm selection, and model training without requiring user expertise in machine learning. The automated exploration process independently evaluates multiple pipeline plans and selects optimal configurations, eliminating the need for users to manually configure complex ML systems.
2Manufacturing precision
If comprehensive ML knowledge is required, then manufacturing precision improves, but ease of manufacture worsens
Solution Approach 1:
The automated pipeline generation system serves as an intermediary that bridges the gap between users without ML expertise and high-quality model requirements. It automatically performs data preprocessing, algorithm selection, and hyperparameter tuning to achieve model quality comparable to expert-level work while requiring minimal user knowledge.
Solution Approach 2:
The system performs preliminary actions by automatically conducting data preprocessing, exploring multiple pipeline plans, and selecting optimal algorithms before the user needs to deploy models. This preliminary automated exploration ensures high model quality is achieved without requiring users to perform complex preparatory work.
3Manufacturing precision
If extensive experimentation is performed, then model quality improves, but loss of time increases
Solution Approach 1:
The system performs preliminary automated exploration of multiple pipeline plans, algorithms, and hyperparameter configurations before model deployment. By pre-exploring and selecting optimal configurations automatically, it achieves high model quality without requiring users to spend extensive time on iterative experimentation.
Solution Approach 2:
The automated exploration process maintains continuous useful action by systematically evaluating multiple pipeline plans and algorithms in sequence, automatically transitioning from data preprocessing to model training and evaluation. This continuous automated process eliminates idle time between experimentation stages while maintaining thorough exploration for model quality.
4Reliability
If specialized ML expertise is required, then reliability improves, but ease of operation worsens
Solution Approach 1:
The automated pipeline generation system acts as an intermediary that ensures reliable model deployment without requiring users to possess specialized ML expertise. It automatically handles algorithm selection, hyperparameter optimization, and model training, guaranteeing reliable results while maintaining operational simplicity for non-expert users.
Data Source
AI summary
Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.