Machine Learning Model Containers for Continuous Provisioning
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Solution Overview
Problem
The increasing size and complexity of machine learning models make it difficult to store, deploy, and provision them effectively, especially in large-scale applications.
Innovation Solution
A continuous provisioning method and apparatus that generate, determine, and manage machine learning models through composed model containers, utilizing a model container builder to identify new models, retrieve associated containers, and determine differences for efficient deployment and updating across a network of computer nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the size and complexity of machine learning models are increased to improve accuracy and quality, then prediction accuracy is improved, but the difficulty of storing, deploying, and provisioning models increases
Solution Approach 1:
The patent segments large-scale machine learning models into multiple smaller model containers that can be independently stored, managed, and deployed. Each model container represents a modular unit containing a portion of the overall model, enabling granular control and simplified provisioning while maintaining the full model's predictive accuracy when containers are assembled together.
2Ease of operation
If traditional model storage and deployment methods are used, then model provisioning becomes more difficult and challenging, but the system structure remains simple
Solution Approach 1:
The patent introduces model containers as intermediary objects between the model training system and the deployment environment. These containers serve as standardized vehicles for model distribution, simplifying the provisioning process by providing a uniform interface for storing, transferring, and deploying models regardless of their size or complexity.
3Productivity
If large-scale models are deployed without containerization, then model accuracy is maintained, but resource usage and deployment efficiency decrease
Solution Approach 1:
The patent applies preliminary action by pre-packaging model containers with all necessary dependencies, configurations, and runtime environments before deployment. This advance preparation eliminates the need for complex on-site model setup and reduces deployment time, while the containerized format optimizes resource utilization through efficient memory management and shared libraries.
Data Source
AI summary
An approach for continuously provisioning machine learning models, executed by one or more computer nodes to provide a future prediction in response to a request from one or more client devices, is provided. The approach generates, by the one or more computer nodes, a machine learning model. The approach determines, by the one or more computer nodes, whether the machine learning model is a new model. In response to determining the machine learning model is not the new model, the approach retrieves, by the one or more computer nodes, one or more model containers with an associated model to a new persistent model. The approach determines, by the one or more computer nodes, a difference between the associated model and the new persistent model. Further, in response to determining the machine learning model is the new model, the approach generates, by the one or more computer nodes, one or more model containers.