API-Based Container Service for Multi-Framework ML Deployment
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Solution Overview
Problem
Existing container technologies do not support efficient integration and deployment of machine learning models with diverse requirements in multi-tenant environments, lacking a standardized interface for seamless integration and management across different frameworks and libraries.
Innovation Solution
Implementing a Bring Your Own Container (BYOC) system with an application programming interface (API) level integration, allowing developers to customize serving containers for machine learning models, supporting multiple frameworks and libraries, and enabling efficient deployment and management in a multi-tenant machine-learning serving infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional containerization is used for machine learning models, then deployment speed is improved, but integration flexibility across different frameworks deteriorates
Solution Approach 1:
The patent implements a universal container service that can host multiple machine learning frameworks (TensorFlow, PyTorch, scikit-learn, etc.) and different application types within the same container infrastructure. The container service acts as a multi-functional platform that accommodates diverse ML workloads through standardized interfaces while maintaining framework-specific capabilities.
Solution Approach 2:
The container service functions as an intermediary layer between the containerized ML applications and the underlying infrastructure. It provides framework-agnostic deployment, management, and orchestration capabilities, allowing different ML frameworks to be integrated through a common interface without requiring direct infrastructure coupling.
2Adaptability or versatility
If custom container implementations are allowed for each framework, then integration flexibility is improved, but system complexity deteriorates
Solution Approach 1:
The patent segments the container service into distinct functional modules: container management, framework execution environments, standardized APIs, and resource orchestration. Each framework (TensorFlow, PyTorch, etc.) runs in isolated container instances with dedicated dependencies, while the overarching service provides unified management, reducing overall system complexity through modular organization.
Solution Approach 2:
A single container service implementation provides universal support for multiple ML frameworks through standardized interfaces and common deployment mechanisms. This eliminates the need for separate custom integration systems for each framework, reducing system complexity while maintaining flexibility through the unified multi-functional platform.
3Ease of operation
If standardized interfaces are implemented at API level, then ease of integration is improved, but development overhead deteriorates
Solution Approach 1:
The patent establishes pre-defined standardized APIs and container templates for common machine learning frameworks and workloads. These standardized interfaces are prepared in advance with common deployment patterns, dependency structures, and execution environments already configured. Developers can leverage these pre-built standards rather than creating integration interfaces from scratch, reducing development overhead while maintaining ease of integration.
Data Source
AI summary
A method and system for an application programming interface (API) based container service for supporting multiple machine learning (ML) applications is described. In particular, a scoring service container includes a base scorer to interface with a ML serving infrastructure using the API. The scoring service container also includes an application specific scorer, which itself includes a model loader and a scoring function. A model identifier is provided to the model loader, and it provides a model object. At least some parameters in a request from a client application are passed to the scoring function, which produces a scoring. The base scorer returns the scoring according to the API to the ML serving infrastructure for delivery to the client application.