ML API Endpoints with Preloaded Model Execution Environments
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Solution Overview
Problem
Conventional systems face inefficiencies in loading machine learning models, requiring several minutes to prepare execution environments, which is too slow for applications needing sub-second decision-making, and involve repetitive processes for different models.
Innovation Solution
A system maintains a ready-to-use execution environment with loaded instances of machine learning models, utilizing virtual machines or containers, and provides API endpoints for rapid access, along with preloading data into memory for sub-second response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional systems load machine learning models sequentially, then model preparation is thorough, but response time exceeds several minutes which is too slow for sub-second decision-making applications
Solution Approach 1:
The system pre-loads machine learning models into execution environments before they are needed. Execution environments are prepared in advance with models already loaded and ready for inference, eliminating the need for sequential model loading during request processing. This preliminary preparation enables sub-second response times by having models ready before client requests arrive.
Solution Approach 2:
The system creates copies of execution environments with loaded models rather than loading models sequentially for each request. Multiple execution environments can maintain copies of models simultaneously, allowing parallel processing of different requests without waiting for sequential model loading to complete.
2Productivity
If the system loads different machine learning models sequentially, then each model is fully prepared, but the repetitive loading process reduces efficiency and increases latency
Solution Approach 1:
Models are loaded into execution environments in advance before client requests arrive. The system maintains a pool of pre-prepared execution environments with models already instantiated, so when requests come in, the system can immediately begin inference without repeating the lengthy model loading process for each different model.
Solution Approach 2:
Execution environments are designed to be multi-functional and can host different machine learning models. A single execution environment framework can accommodate multiple models, allowing the system to serve different decision-making needs through the same infrastructure without requiring separate loading processes for each model type.
3Speed
If the system maintains ready-to-use execution environments with loaded models, then sub-second response times are achieved, but system complexity increases due to virtual machine or container management
Solution Approach 1:
The system introduces an intermediary layer between the client and the machine learning models - the execution environment acts as a mediator that handles model loading, memory management, and request routing. This intermediary abstraction layer manages the complexity of virtual machine or container orchestration, presenting a simplified interface for rapid model inference while handling the underlying complexity automatically.
Data Source
AI summary
One or more virtual machines are launched at an application platform. At each of the one or more virtual machines, a machine learning model execution environment is instantiated for an instance of a machine learning model. A respective instance of the machine learning model is loaded to each machine learning model execution environment. Each loaded instance of the machine learning model is associated with an application programming interface (API) endpoint which can receive input data for the loaded instance of the machine learning model from a client device and return output data produced by the loaded instance of the machine learning model based on the input data.