ML Pipeline Corpus Augmentation via Model Substitution
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Solution Overview
Problem
Current automatic generation techniques for machine learning pipelines are inefficient, requiring significant computational resources and time, and often fail to produce accurate pipelines due to the manual creation methods employed by data scientists, which lack optimal model selection and contain varied quality.
Innovation Solution
A system that receives a machine learning project, mutates a first ML pipeline by substituting its model with a second model from a predefined set, selects pipelines based on performance scores, and augments the corpus database with high-performing pipelines, using meta-learning models to learn from human-written pipelines and generate new, high-quality pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current automatic generation techniques for ML pipelines are used, then pipelines can be generated automatically, but computational time and resources are significantly consumed
Solution Approach 1:
The system performs preliminary actions by pre-processing human-written ML pipelines into intermediate representations and pre-training the meta-learning model on a corpus of existing pipelines before actual pipeline generation. This preliminary preparation enables the model to quickly generate new pipelines without requiring extensive computational search during the actual generation phase, thus reducing computational time while maintaining automation.
2Extent of automation
If current automatic generation techniques for ML pipelines are used, then pipelines can be generated automatically, but accuracy of generated pipelines is insufficient
Solution Approach 1:
The system incorporates feedback mechanisms by evaluating generated pipelines against performance metrics and using this feedback to iteratively improve the meta-learning model. The model learns from the outcomes of previous pipeline generations, adjusting its parameters to produce more accurate pipelines in subsequent iterations, thereby improving pipeline accuracy while maintaining automation.
Solution Approach 2:
The meta-learning model performs self-service by autonomously learning from the corpus of human-written pipelines and automatically improving its own pipeline generation capabilities without requiring manual intervention. The model self-adjusts its parameters and structures based on learned patterns, enabling it to generate accurate pipelines automatically.
3Reliability
If manual creation of ML pipelines by data scientists is used, then pipelines can be created with expert knowledge, but significant effort and time are required
Solution Approach 1:
The system uses copying by replicating patterns and structures from existing high-quality human-written ML pipelines. The meta-learning model learns from a corpus of human-created pipelines and copies successful patterns, adapting them to new datasets and problems. This allows the system to produce reliable pipelines with expert-level quality while maintaining high productivity, as the model can rapidly generate pipelines without requiring expert manual effort for each new case.
4Manufacturing precision
If exploration of vast space of possible ML pipelines is performed, then optimal pipeline can be found, but computation resources are significantly consumed
Solution Approach 1:
The system applies parameter changes by transforming the search space from considering all possible pipeline configurations to focusing on a reduced set of promising candidates generated by the meta-learning model. The model changes the parameters of pipeline generation by leveraging learned patterns from the corpus, enabling optimal pipeline selection with significantly reduced computational resources compared to exhaustive exploration of the vast pipeline space.
Data Source
AI summary
According to an aspect of an embodiment, operations may include receiving an ML project stored in an ML corpus database. The operations may further include mutating a first ML pipeline, of a set of first ML pipelines associated with the received ML project, to determine a set of second ML pipelines. The mutation of the first ML pipeline may correspond to a substitution of a first ML model associated with the first ML pipeline with a second ML model associated with one of the set of predefined ML pipelines. The operations may further include selecting one or more ML pipelines from the set of second ML pipelines based on a performance score associated with each of the determined set of ML pipelines. The operations may further include augmenting the ML corpus database to include the selected one or more ML pipelines and the set of first ML pipeline.