Centralized Model Training Device for ML Development
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Solution Overview
Problem
The conventional development process for machine learning models is inefficient, time-consuming, and prone to errors due to manual setup, optimization, and sharing processes, which can lead to inconsistent results and increased model risk.
Innovation Solution
A computer-implemented method utilizing a centralized model training device that enables remote users to efficiently access, upload, create, test, publish, and share machine learning models and datasets through a graphical user interface, reducing manual processes and introducing an adjustable model management system for seamless integration and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for setting up environments, training models, and sharing models, then flexibility and control are maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The patent introduces a model training device as an intermediary system between users and machine learning environments. This device provides a standardized interface and automated workflows that mediate the model development process, eliminating manual setup variations while maintaining user control through a unified interface.
Solution Approach 2:
The system changes the parameters of model development by providing pre-configured environments with standardized library versions, data formats, and training parameters. This standardization transforms the development process from manual parameter setting to automated parameter management, improving both speed and consistency.
2Ease of operation
If programmers set up environments from scratch with significant knowhow, then customization and control are achieved, but time investment and skill requirements increase
Solution Approach 1:
The model training device performs preliminary actions by pre-configuring machine learning environments with necessary libraries, tools, and dependencies before users need them. This eliminates the need for programmers to set up environments from scratch, significantly reducing setup time and skill requirements while maintaining customization options.
Solution Approach 2:
The system uses copying by providing reusable, pre-configured environment templates that can be replicated across multiple projects and users. Instead of creating environments from scratch each time, the system copies and adapts standardized templates, reducing both time investment and skill requirements.
3Reliability
If manual optimization processes are used for machine learning models, then fine-grained control is maintained, but results become inconsistent and time-consuming
Solution Approach 1:
The model training device implements automated feedback mechanisms that monitor model training progress, performance metrics, and resource utilization in real-time. This feedback enables the system to automatically adjust optimization parameters and identify best practices, ensuring consistent results across different models and users while accelerating the optimization process.
4Ease of operation
If models are shared by manually copying files and libraries, then direct control over distribution is maintained, but errors and time consumption increase
Solution Approach 1:
The system implements automated copying mechanisms that replicate models, dependencies, and configurations through standardized protocols. This eliminates manual file copying errors while maintaining version control and integrity, making model sharing as easy as initiating a transfer command while ensuring accuracy through automated verification.
Data Source
AI summary
An embodiment of the present invention relates to computer implemented methods and systems for receiving, by a model training device, a request for utilizing an adjustable management system by a user at a client device; wherein the client device is connected to the model training device over a network; sharing, by the model training device, an adjustable model management system comprising machine learning models and corresponding datasets in response to the received request to the client device; receiving an action from the client device, the action selected from a plurality of machine learning model services selected from the adjustable model management system; and executing an action by the model training device, to effect a change to a machine learning model saved into memory on the model training device, based on the received action.