Predictive ML Model Prefetching for Rapid User Deployment
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Solution Overview
Problem
Existing machine learning (ML) model deployment and management systems are inefficient and resource-intensive, failing to quickly deploy models for user needs in real-time business decision-making applications.
Innovation Solution
A system that derives user patterns from end-user interactions with ML models, using cognitive self-learning to automatically deploy selected models based on these patterns, optimizing resource usage and reducing deployment time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional ML model deployment systems are used, then models can be deployed, but deployment time is long and resource usage is high
Solution Approach 1:
The system performs preliminary actions by analyzing user access patterns and pre-deploying frequently accessed ML models before they are actually needed. The model deployment manager proactively identifies high-demand models based on historical data and deploys them in advance to edge devices, eliminating the need for on-demand deployment delays.
Solution Approach 2:
The deployment system dynamically adapts to changing user needs by continuously monitoring access patterns and adjusting model deployment decisions in real-time. The model deployment manager modifies deployment strategies based on current traffic conditions, user behavior changes, and resource availability, making the system flexible and responsive.
2Adaptability or versatility
If all ML models are deployed to meet user needs, then model availability is improved, but resource consumption increases
Solution Approach 1:
The system applies local quality by deploying different sets of ML models to different edge devices based on their specific user bases and access patterns. Instead of uniformly deploying all models to all devices, the model deployment manager tailors deployments to local needs, ensuring high availability where needed while conserving resources elsewhere.
Solution Approach 2:
The system changes deployment parameters dynamically based on monitored metrics. The model deployment manager adjusts deployment decisions by changing parameters such as model selection, deployment timing, and resource allocation based on real-time analysis of user behavior patterns and system resource states.
3Extent of automation
If manual model deployment management is used, then deployment control is maintained, but automation level is low and efficiency suffers
Solution Approach 1:
The system implements feedback loops where the model deployment manager continuously monitors deployment performance, user access patterns, and system metrics. This feedback is used to automatically adjust and optimize deployment decisions, creating a self-improving system that learns from operational data and refines its strategies over time.
Solution Approach 2:
The deployment system performs self-service by automatically analyzing user behavior data, identifying deployment opportunities, and executing model deployments without manual intervention. The model deployment manager autonomously manages the entire deployment lifecycle, from pattern recognition to model selection and deployment execution.
Data Source
AI summary
A method for receiving an end-user model access data set, deriving a plurality of patterns of actions typically performed by the end-user based on analysis of the end-user model access data set, and deriving a first model deployment protocol to automatically deploy selected ML models of the plurality of ML models for the end-user when the end-user works with ML models based on the plurality of patterns of actions.