ML Training Job Management via Standardized Control Interfaces
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Solution Overview
Problem
Current mobile and wireless telecommunication systems lack efficient means to manage machine learning model training, particularly in cognitive autonomous networks, where specific inputs and training features are required for each use case, necessitating a standardized approach to request, control, and report on machine learning training processes.
Innovation Solution
The implementation of a method and apparatus for managing machine learning training jobs, allowing consumers to request and control the instantiation of machine learning training jobs, with notification mechanisms for job status and reporting, utilizing a standardized information model that includes attributes for managing and controlling ML training processes within the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning training is implemented in cognitive autonomous networks, then the network can support specific use cases with customized training features, but the system lacks standardized means to manage and control training processes
Solution Approach 1:
The patent implements a universal management framework that handles diverse machine learning training requests through standardized interfaces. The system provides common functionalities for job submission, status monitoring, and result retrieval that work across different use cases and model types, reducing management complexity while maintaining adaptability.
Solution Approach 2:
The system manages training complexity by parameterizing training jobs with configurable attributes such as model type, training data, hyperparameters, and resource allocation. This allows flexible customization for specific use cases while maintaining standardized control mechanisms through consistent parameter management.
2Productivity
If standardized management interfaces are implemented, then training processes can be controlled efficiently, but the system may lack flexibility for specific training requirements
Solution Approach 1:
The standardized management interfaces are designed to be multi-functional, handling various training scenarios through a unified API. The system can submit different types of training jobs (classification, regression, clustering), monitor diverse model training processes, and retrieve various output formats, maintaining both efficiency and flexibility.
Solution Approach 2:
The management system dynamically adapts to specific training requirements while maintaining standardized interfaces. It can adjust resource allocation, modify training parameters, and scale computing resources based on the specific needs of each training job, ensuring flexibility without compromising management efficiency.
3Adaptability or versatility
If multiple training jobs are instantiated simultaneously, then more use cases can be supported, but resource management and job coordination become more complex
Solution Approach 1:
The patent introduces a management system as an intermediary between training job submissions and actual training execution. This mediator handles job queuing, resource allocation, and coordination, allowing multiple training jobs to run simultaneously while simplifying the complexity of job management and resource coordination.
Data Source
AI summary
Systems, methods, apparatuses, and computer program products for managing machine learning training. One method may include a machine learning training function receiving a request to instantiate a machine learning training job from a consumer, and instantiating the requested machine learning training job. The machine learning training function may transmit a notification to the consumer indicating that the machine learning training job has been instantiated.