Unified Pipeline Flow with Phase-Specific Paths
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Solution Overview
Problem
In pipeline development for statistical and machine learning models, separate pipelines for different deployment phases (such as experimentation, training, and scoring) lead to challenges in deployment, reduce explainability, and hinder auditability.
Innovation Solution
A unified pipeline flow with common and phase-specific paths is introduced, allowing for a single design paradigm that declaratively represents common and phase-specific tasks, enabling high automation, visual explainability, and simplified manageability across all deployment phases without the need for coding forked paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate pipelines are developed for different deployment phases (experimentation, training, scoring), then each phase can be optimized independently, but deployment complexity increases and explainability is reduced
Solution Approach 1:
The pipeline is segmented into phase-specific paths (experimentation path, training path, scoring path) that can be independently optimized while sharing common components. Each path is defined with phase-specific parameters and tasks, allowing independent optimization without creating separate complete pipelines.
Solution Approach 2:
Common components across all phases (data sources, transformations, features) are merged into a single unified pipeline structure. The common path handles shared operations, while phase-specific paths branch off only when needed, reducing overall complexity compared to separate pipelines.
2Ease of manufacture
If separate pipelines are developed for different deployment phases, then phase-specific tasks can be isolated, but auditability and explainability are reduced
Solution Approach 1:
The pipeline graph is segmented into distinct phase-specific paths (common path, experimentation path, training path, scoring path) with clear visual separation. Each path can be independently configured and audited while maintaining visibility within the unified structure, enabling both isolation and transparency.
Solution Approach 2:
Different paths in the unified pipeline are visually distinguished through color coding (e.g., different colors for common path, experimentation path, training path, scoring path). This visual differentiation enhances explainability and auditability by making the flow of data and decisions transparent across phases.
3Ease of operation
If separate pipelines are developed for different deployment phases, then each phase can be independently managed, but productivity decreases due to repeated work
Solution Approach 1:
Common components (data sources, transformations, features) are merged into a single unified pipeline that serves all phases. This eliminates redundant work in developing and maintaining common infrastructure, significantly improving productivity while allowing independent management of phase-specific tasks.
Solution Approach 2:
The unified pipeline is designed as a multi-functional platform that can execute all phases (experimentation, training, scoring) through a single deployment. The same pipeline infrastructure handles multiple purposes, reducing overall development time and resource consumption compared to separate specialized pipelines.
4Adaptability or versatility
If coding forked paths is used to handle different phases, then phase-specific logic can be implemented, but complexity and maintenance difficulty increase
Solution Approach 1:
Phase-specific logic is segmented into distinct, visually separable paths within the unified pipeline graph. Each path has clear boundaries and can be independently configured without affecting other phases, reducing code complexity while maintaining adaptability.
Solution Approach 2:
The pipeline uses dynamic routing based on phase parameters rather than hard-coded forked paths. The system automatically selects the appropriate path (common, experimentation, training, or scoring) based on the deployment phase parameter, making the logic more maintainable and easier to modify.
Data Source
AI summary
Systems, methods, and other embodiments associated with associated with unified pipeline flow with common and phase-specific paths are described. In one embodiment, a method includes accepting, through a graphical user interface, a setting of a phase-specific link type for a link between nodes of a pipeline, wherein the phase-specific link type indicates that the link is associated with a particular phase; accepting, through the graphical user interface, a selection to execute the pipeline for the phase; parsing the pipeline to determine an execution set of nodes for execution in the phase based on the nodes being connected with links having either the link type or a default link type common to all phases; and executing the pipeline for the phase by executing the execution set of nodes, and not executing nodes not included in the set of nodes.