Computer Vision Model Execution Service Standardized Interface
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Solution Overview
Problem
Users of computer vision machine learning models face challenges in efficiently utilizing these models due to variability in input and output requirements, lack of awareness about suitable models, and inefficient resource utilization, leading to limited functionality in computing systems.
Innovation Solution
A computer vision model execution service provides an interface for users to discover, invoke, and manage machine learning models through a standardized API, enabling the determination of workflows that produce desired output vectors by identifying model dependencies and specifying execution orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple computer vision machine learning models are provided with different input and output requirements, then model functionality and versatility are improved, but system complexity and difficulty of operation increase
Solution Approach 1:
The patent implements a universal execution service that can handle multiple different computer vision machine learning models through a single standardized interface. The service abstracts away model-specific variations in input/output requirements, allowing users to invoke diverse models without needing to understand their individual complexities. This resolves the contradiction by providing model versatility while maintaining interface simplicity.
Solution Approach 2:
The execution service acts as an intermediary layer between the user and the diverse machine learning models. It manages the complexity of different model requirements by handling input validation, preprocessing, and output formatting internally. Users interact only with the standardized interface, while the intermediary service translates these generic requests into model-specific operations, thereby reducing operational difficulty without limiting model functionality.
2Adaptability or versatility
If users are provided with access to multiple machine learning models, then model selection flexibility is improved, but ease of operation deteriorates due to lack of awareness about suitable models
Solution Approach 1:
The execution service incorporates feedback mechanisms that provide users with information about available models, their capabilities, and suitability for specific tasks. The service can recommend appropriate models based on user needs and provide feedback on model performance and resource utilization. This helps users make informed decisions about model selection without overwhelming them with complexity, thereby maintaining both flexibility and ease of operation.
3Productivity
If machine learning models are executed without standardized interface, then resource utilization efficiency deteriorates, but implementation simplicity is improved
Solution Approach 1:
The execution service provides a universal standardized interface that optimizes resource utilization across multiple machine learning models. By implementing common preprocessing, execution management, and postprocessing functions at the service level, it eliminates redundant operations and improves computational efficiency. The standardized interface adds a layer of complexity, but this complexity is centralized and managed, allowing individual model implementations to remain simple while achieving overall resource optimization.
Data Source
AI summary
Systems and methods are provided for implementing a computer vision model execution service. A computer vision model execution service may maintain a library of machine learning models, and may provide a standard interface for accessing a model or models. Models may implement schemas that specify an input vector and an output vector, and the computer vision model execution service may obtain or determine workflows that process input vectors through multiple computer vision models. The service further provides an interface for adding, removing, or updating models, and may provide feedback to modelers regarding the usage and performance of various machine learning models.