ML Workflow Clustering via Coordinate Embedding
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Solution Overview
Problem
Current machine learning workflow management systems face cognitive overload due to complexity, as they primarily focus on graph properties without considering operational and temporal aspects, limiting their ability to accurately cluster and automate parallel workflows.
Innovation Solution
The system embeds workflow graphs in a coordinate system, allowing for comparison based on operation type, algorithm, data processing, and infrastructure properties, enabling accurate clustering and automation by considering contextual information such as sequential and temporal dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning workflows are managed using traditional graph-based approaches focusing only on graph properties, then the system structure is simple, but the accuracy of workflow clustering and automation is insufficient due to lack of operational and temporal context
Solution Approach 1:
The patent transforms the traditional graph-based workflow representation by embedding it in a coordinate system that adds temporal and operational dimensions. Workflows are represented with coordinates including operation type, algorithm, data processing characteristics, and infrastructure properties, enabling multi-dimensional comparison and clustering that captures both structural and contextual information.
Solution Approach 2:
The patent introduces new parameters for workflow representation beyond graph structure, including operation type, algorithm used, data processing characteristics, and temporal dependencies. These parameter changes enable more precise workflow clustering by capturing operational and contextual nuances that traditional graph properties miss.
2Adaptability or versatility
If multiple machine learning algorithms and ETL processes are integrated into complex workflows, then the functionality and versatility of the system is improved, but the cognitive load on users increases due to the complexity of managing interactions between numerous components
Solution Approach 1:
The patent enables workflows to be automatically clustered and analyzed through machine learning algorithms that process the embedded coordinate information. The system self-organizes workflows into clusters based on operational and temporal patterns, reducing the need for manual analysis and management of complex interactions between multiple algorithms and ETL processes.
Solution Approach 2:
The patent implements a feedback mechanism where machine learning algorithms analyze workflow patterns and provide insights about operational dependencies and temporal relationships. This feedback helps users understand and manage complex workflows by highlighting patterns and relationships that would be difficult to detect manually.
3Productivity
If workflows are clustered based only on graph properties without considering operational properties, then the clustering process is computationally efficient, but the accuracy of workflow segmentation and pattern recognition is insufficient
Solution Approach 1:
The patent segments workflow analysis into multiple independent dimensions: graph structure, operation type, algorithm characteristics, data processing properties, and temporal dependencies. Each dimension can be processed separately and then integrated, maintaining computational efficiency while improving pattern recognition accuracy through multi-faceted analysis.
Solution Approach 2:
The patent adds operational and temporal dimensions to the traditional graph-based workflow representation. By embedding workflows in a coordinate system that includes operation type, algorithm, and temporal information, the system enables more accurate pattern recognition while maintaining computational tractability through structured multi-dimensional comparison.
Data Source
AI summary
A system and method of constructing a machine learning workflow by using machine learning suggestions derived from determining path lengths in a plurality of existing workflows, assigning a frequency threshold for each path and determining a probability for each path. This information is utilized to determine transpositions and deletions between paths that can be used as training for a machine learning algorithm that will suggest to the user which operators to put in a new machine learning workflow.