ML Model Layer for Framework-Agnostic Execution and Resource Allocation
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Solution Overview
Problem
Data scientists face significant effort in onboarding machine learning models to production infrastructure, lack flexibility in using frameworks of their choice, and struggle with managing model execution logic and hardware resource allocation.
Innovation Solution
A machine learning model layer that allows submission of models without defining execution logic, using configuration metadata to manage model execution, allocate resources, and batch predictions, enabling efficient use across different frameworks and hardware types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data scientists manually onboard machine learning models to production infrastructure and write model execution code, then model execution control and customization are improved, but development time and operational complexity increase significantly
Solution Approach 1:
The patent introduces an intermediary layer (model execution environment with standardized interfaces) between the machine learning model and production infrastructure. This intermediary handles the complexity of model execution, resource allocation, and infrastructure management automatically, allowing data scientists to submit models without writing execution code while maintaining control through standardized configuration mechanisms.
Solution Approach 2:
The patent extracts the model execution logic and infrastructure management tasks from the data scientist's workflow. By separating model submission from model execution implementation, the system removes the burden of writing execution code and managing production infrastructure from data scientists, while still providing full control through configuration-based mechanisms.
2Adaptability or versatility
If data scientists use multiple different machine learning frameworks, then model development flexibility and capability are improved, but deployment complexity and infrastructure management increase
Solution Approach 1:
The patent creates a universal model execution environment that can execute machine learning models from multiple different frameworks (TensorFlow, PyTorch, scikit-learn, etc.) through a standardized interface. This universal layer handles framework-specific complexities internally, allowing data scientists to use their preferred frameworks without increasing deployment complexity or infrastructure management burden.
3Productivity
If machine learning models are deployed in multi-tenant environments with multiple users, then system utilization and resource efficiency are improved, but resource allocation management and isolation complexity increase
Solution Approach 1:
The patent segments the multi-tenant system into isolated execution environments where each user's machine learning models run in separate, managed containers or virtual environments. This segmentation provides automatic resource isolation and allocation management, allowing high system utilization through shared infrastructure while maintaining tenant isolation without requiring complex manual management of resource allocation and access control.
Data Source
AI summary
Techniques are disclosed that pertain to facilitating the execution of machine learning (ML) models. A computer system may implement an ML model layer that permits ML models built using any of a plurality of different ML model frameworks to be submitted without a submitting entity having to define execution logic for a submitted ML model. The computer system may receive, via the ML model layer, configuration metadata for a particular ML model. The computer system may then receive a prediction request from a user to produce a prediction based on the particular ML model. The computer system may produce a prediction based on the particular ML model. As a part of producing that prediction, the computer system may select, in accordance with the received configuration metadata, one of a plurality of types of hardware resources on which to load the particular ML model.