ML Pipeline Orchestrator for Failure Mode Analytics
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Solution Overview
Problem
Organizations struggle to analyze and utilize historical data from physical assets, such as machinery and equipment, to identify failure modes and improve maintenance practices due to the vast amount of unstructured text data buried within notifications and work orders.
Innovation Solution
A system for failure mode analytics is developed, utilizing machine learning to create a model that extracts topics from historical data, maps them to predefined failure modes, and provides a user interface for validation and scoring, enabling proactive maintenance by predicting asset failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained and validated multiple times, then model accuracy and reliability improve, but computational time and processing resources increase
Solution Approach 1:
The orchestrator performs preliminary actions by pre-configuring pipeline parameters, selecting appropriate models in advance, and preparing validation datasets before the actual training and validation processes begin. This reduces the time required during execution while maintaining model accuracy.
Solution Approach 2:
The system dynamically adjusts the number of training iterations, validation frequency, and resource allocation based on real-time performance metrics and convergence criteria. This allows the system to optimize the balance between model accuracy and computational time by adapting the process to the specific characteristics of each dataset and model.
2Reliability
If multiple machine learning pipelines are executed with different parameters, then model performance is optimized, but system complexity increases
Solution Approach 1:
The orchestrator serves multiple functions: it manages pipeline execution, configures parameters, selects models, monitors training progress, and validates results. This universal system handles diverse machine learning pipelines through a unified interface, reducing the apparent complexity for users while enabling optimized model performance through multiple execution variants.
Solution Approach 2:
The orchestrator acts as an intermediary layer between the user and the complex machine learning pipeline execution processes. It abstracts away the complexity of managing multiple pipelines with different parameters by providing a simplified interface that automatically handles parameter configuration, model selection, and execution coordination.
3Measurement precision
If extensive validation and scoring processes are performed, then model quality is ensured, but processing time increases
Solution Approach 1:
The system performs partial validation and scoring by selecting key metrics and essential validation steps based on the specific pipeline configuration and performance requirements. This approach ensures adequate model quality control without executing every possible validation check, thereby reducing processing time while maintaining necessary quality standards.
Data Source
AI summary
Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.