ML Pipeline Annotation with Operational Semantics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning pipeline technologies face challenges in efficiently managing complex multi-step analytics and training multiple models concurrently, as they lack effective methods for annotating operational semantics and leveraging lineage semantics to optimize model training and selection.
Innovation Solution
A computer system and method that represent a machine learning pipeline in a directed acyclic graph (DAG) or data flow graph (DFG), selectively annotating nodes with operational and lineage semantics to pre-process the pipeline, allowing concurrent training and selection of models based on performance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning pipelines are represented without operational semantics annotations, then the pipeline structure is simple, but the system cannot efficiently manage complex multi-step analytics or optimize model training
Solution Approach 1:
The patent applies preliminary action by annotating pipeline nodes with operational semantics (such as transformation types, data formats, and processing operations) before executing the machine learning pipeline. This pre-annotation enables the system to efficiently manage complex multi-step analytics by having all necessary operational information prepared in advance, allowing the execution engine to optimize model training and selection without encountering information gaps during runtime.
2Loss of time
If multiple machine learning models are trained sequentially, then the training process is simple to manage, but the time required to train and select models increases
Solution Approach 1:
The patent applies dynamics by enabling concurrent training of multiple machine learning models based on annotated operational semantics. The system dynamically determines which models can be trained in parallel by analyzing the annotated pipeline structure, allowing flexible adaptation of the training schedule. This dynamic approach reduces the time required to train and select models while managing complexity through semantic annotations that guide the concurrent execution strategy.
3Reliability
If lineage semantics are not annotated on pipeline nodes, then the annotation process is simpler, but the system cannot effectively associate training data sets with testing data sets
Solution Approach 1:
The patent applies preliminary action by annotating pipeline nodes with lineage semantics that trace the origin and transformation of data sets through the pipeline. This pre-annotation of data lineage information enables the system to accurately associate training data sets with corresponding testing data sets by following the annotated lineage paths. The lineage annotations are established before model training, ensuring reliable data set associations without requiring complex runtime analysis.
Data Source
AI summary
A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning (ML) pipeline, is implemented over a data flow graph (DFG) with nodes configured to support rich semantics. The rich semantics include two or more operational semantics, and at least one lineage semantic to selectively combine features that trace lineage to a common input object. The lineage semantic is leveraged to associate training and testing data set pairs in cross validation of the trained ML models produced from parallelizing the selection of ML pipelines.