Graphical Modular Machine Learning Pipeline Architecture
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Solution Overview
Problem
The implementation of machine learning algorithms is hindered by the lack of necessary skills and expertise, leading to increased time and suboptimal outcomes, particularly in complex development and manufacturing operations involving multiple teams.
Innovation Solution
A computing platform with a graphical user interface (GUI) allows users to architect machine learning pipelines by linking various aspects, including data integration and machine learning modules, enabling the construction of machine learning algorithms through a network of interconnected software components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional machine learning implementation approaches are used, then technical expertise is required, but this increases development time and reduces accessibility
Solution Approach 1:
The patent segments the machine learning pipeline into distinct modular components including data ingestion modules, feature engineering modules, model training modules, and evaluation modules. Each module is independently developable and interchangeable, allowing users to assemble pipelines without requiring deep expertise in all areas. This modular segmentation directly addresses the contradiction by making the system easier to operate while reducing the time needed to build functional pipelines through component reuse.
Solution Approach 2:
The patent provides pre-built modules and templates that encapsulate common machine learning tasks. Users can select from pre-implemented data ingestion, feature engineering, and model training modules that are ready to use, eliminating the need to implement these functions from scratch. This preliminary action approach directly reduces development time while maintaining ease of operation through simple module selection and configuration.
2Adaptability or versatility
If skilled expertise is required for machine learning algorithms, then model accuracy can be improved, but this limits the number of teams that can effectively use the tool
Solution Approach 1:
The patent enables users to build and configure their own machine learning pipelines through intuitive graphical interfaces without requiring specialized expertise. The self-service capability allows multiple teams to independently create, modify, and deploy pipelines using standardized modules. Meanwhile, the system maintains reliability through built-in validation mechanisms, automated testing frameworks, and best practice templates that ensure model accuracy even when used by teams without deep ML expertise.
Solution Approach 2:
The patent creates a universal platform with standardized interfaces and interchangeable modules that can be used across different teams and applications. The same core modules can serve multiple purposes through configurable parameters and adapters. This universality directly increases the number of teams that can effectively use the tool while maintaining model accuracy through consistent, validated implementation patterns that work across all use cases.
3Productivity
If complex machine learning pipelines are implemented, then processing capability is improved, but this increases the complexity of the system
Solution Approach 1:
The patent divides complex processing tasks into discrete, manageable modules that handle specific functions such as data ingestion, transformation, feature engineering, and evaluation. Each module has well-defined inputs and outputs, making the overall system easier to understand and maintain. This segmentation allows the system to achieve high processing capability through modular composition while keeping individual components simple and the architecture clear.
Solution Approach 2:
The patent introduces standardized connectors and data formats as intermediaries between modules. These intermediaries handle the complexity of data type conversion, error handling, and communication protocols, allowing modules to be interconnected without exposing the underlying complexity. The intermediary layer enables high processing capability through module composition while maintaining low system complexity from the user's perspective.
Data Source
AI summary
A method of architecting machine learning pipelines is provided. Example implementations of the method include causing an apparatus to generate a graphical user interface (GUI) from which a computing platform is accessible to architect machine learning pipelines. In example implementations, the method includes for a machine learning pipeline for a phase in the machine learning lifecycle: building software components that are separate, distinct and encapsulate respective processes executable to implement the phase in the machine learning lifecycle, the software components including ports that are communication endpoints of the software components. The method further includes interconnecting the software components with connections attached to the ports and thereby forming a network of interconnected software components that embodies the machine learning pipeline. The method also includes executing the machine learning pipeline and thereby the network of interconnected software components, and thereby implementing the phase in the machine learning lifecycle.