Containerized ML Model Deployment for Failure Isolation
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Solution Overview
Problem
Existing systems for deploying and running machine learning models lack efficient deployment mechanisms, leading to increased development time, resource consumption, and scalability issues, particularly when models fail or crash, causing instability in single-cluster environments.
Innovation Solution
Utilizing container technology to decouple machine learning model deployment from local client machines to a computing environment, providing process isolation and resource management, enabling faster and easier scaling and updating of models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If machine learning models are deployed in a single-cluster environment, then deployment is simplified, but system stability deteriorates when models fail or crash
Solution Approach 1:
The patent applies segmentation by dividing the model serving infrastructure into multiple independent clusters. Each cluster operates autonomously, so when a model fails in one cluster, it does not affect other clusters. This segmentation resolves the contradiction by maintaining deployment simplicity while improving system stability through isolation of failures.
Solution Approach 2:
The patent implements local quality by enabling different clusters to run different versions or instances of the same model. Each cluster can be optimized independently for specific workloads or failure scenarios. This allows the system to maintain overall stability by routing traffic to healthy clusters while preserving deployment flexibility.
2Reliability
If multiple clusters are used for model deployment, then system reliability improves, but deployment complexity increases
Solution Approach 1:
The patent applies universality by creating a standardized multi-cluster architecture that can serve multiple purposes: fault isolation, load distribution, and version management. The same cluster infrastructure serves all these functions simultaneously, reducing the overall deployment complexity despite using multiple clusters for improved reliability.
3Speed
If machine learning models are deployed locally on client machines, then deployment speed is fast, but resource utilization and scalability are limited
Solution Approach 1:
The patent introduces a multi-cluster infrastructure as an intermediary between local client machines and the actual model execution environment. This intermediary enables fast deployment to the cluster while providing centralized resource management and optimization, thus resolving the contradiction between deployment speed and resource utilization efficiency.
Data Source
AI summary
The subject technology receives a first request to deploy a first machine learning model, the first request including information indicating an identifier associated with the first machine learning model. The subject technology determines, based at least in part on the first machine learning model, a set of dependent machine learning models. The subject technology selects a first container environment to deploy the first machine learning model and the set of dependent machine learning models. The subject technology, based on the selected first container environment, executes an instance of an application in the selected first container environment, the first machine learning model and the set of dependent machine learning models being deployed as part of the instance of the application.