ML Task Orchestration Service for Inference Flow Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing the flow of inference results between multiple machine learning models in complex applications is challenging, as developers often lack awareness of other models' runtime environments and how to transfer results effectively, leading to difficulties in combining and coordinating these models efficiently.
Innovation Solution
A machine learning task orchestration service (MLTOS) coordinates the execution of multiple machine learning tasks by creating inference graphs, selecting appropriate runtime environments, and managing parallelism, failures, and data flow without requiring developers to handle resource management or inter-task coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are developed independently by different data scientists, then domain expertise can be effectively utilized for each model, but managing the flow of inference results between models becomes complex and difficult
Solution Approach 1:
The patent introduces an orchestration service as an intermediary component that manages the flow of inference results between independently developed machine learning models. This mediator handles task coordination, data flow routing, and resource management, allowing models to be developed independently while simplifying their integration through a centralized coordination layer.
Solution Approach 2:
The system segments the machine learning pipeline into independent task units that can be developed, tested, and deployed separately. Each model is encapsulated as an independent task with well-defined interfaces, enabling parallel development by different data scientists while the orchestration service manages their interconnections.
2Ease of operation
If developers manually manage resource allocation and inter-task coordination for machine learning models, then fine-grained control over execution is achieved, but development time and processing overhead increase significantly
Solution Approach 1:
The orchestration service implements self-service mechanisms where tasks automatically register themselves, declare their resource requirements and dependencies, and receive automated scheduling and coordination. This eliminates manual configuration overhead while maintaining execution control flexibility through programmatic interfaces.
Solution Approach 2:
The system performs preliminary actions by pre-registering tasks with the orchestration service, which automatically analyzes dependencies and prepares execution plans before actual inference runs. This advance preparation reduces development time by eliminating manual coordination setup while preserving execution flexibility.
3Reliability
If machine learning tasks are executed sequentially to ensure proper coordination, then correctness is maintained, but processing speed decreases
Solution Approach 1:
The orchestration service dynamically determines execution ordering based on task dependencies and data flow requirements. Tasks that are independent are automatically executed in parallel, while dependent tasks are scheduled sequentially. This dynamic approach maintains correctness through dependency-aware scheduling while maximizing processing speed through parallel execution where possible.
Solution Approach 2:
The system implements feedback mechanisms where the orchestration service continuously monitors task execution status and dynamically adjusts scheduling decisions. Based on feedback about completed tasks and available resources, the service optimizes the execution plan to maintain correctness while improving processing speed through adaptive parallelization.
Data Source
AI summary
Descriptors of machine learning tasks to be used to respond to analysis requests, indicating acceptable categories of runtime environments for the tasks and metrics to be collected from the tasks, are received via programmatic interfaces. In response to an analysis request, an orchestrator receives results from individual tasks as they become available, provides the results to other tasks, and causes a response to the request to be prepared using results from at least a subset of the tasks. Metrics collected from the tasks, and a visual representation of the tasks indicating their runtime environments are presented.