Machine Learning API Endpoints with Preloaded Execution Environments
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Solution Overview
Problem
Conventional systems experience slow loading times and inefficiencies when launching machine learning models, making it difficult to support applications requiring sub-second decision-making based on model output scores.
Innovation Solution
A system that maintains a ready-to-use execution environment with loaded instances of machine learning models, accessible via API endpoints, and preloads relevant data into memory to reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If machine learning models are loaded into execution environment at runtime, then system resource usage is optimized, but request processing time increases
Solution Approach 1:
The system pre-loads machine learning models into execution environments before they are needed. The execution environment manager maintains multiple execution environments with models already loaded, so when a prediction request arrives, the model is immediately available for processing without runtime loading delays.
2Speed
If multiple execution environments are maintained with loaded models, then request processing speed improves, but memory consumption increases
Solution Approach 1:
The execution environment manager dynamically manages multiple execution environments, loading and unloading models based on demand. When prediction requests are queued, execution environments with loaded models are allocated. When environments are no longer needed, models are unloaded to free memory resources, creating a dynamic balance between speed and memory usage.
3Productivity
If models are loaded on-demand, then memory efficiency is improved, but service latency increases
Solution Approach 1:
The system performs preliminary actions by pre-loading models into execution environments before prediction requests arrive. The execution environment manager monitors the queue of incoming requests and ensures that sufficient execution environments have models loaded and ready, preventing latency caused by on-demand model loading.
Solution Approach 2:
The system maintains continuous readiness of execution environments with loaded models. Rather than loading models intermittently based on individual requests, the execution environment manager keeps a pool of execution environments continuously prepared with models loaded, ensuring uninterrupted and low-latency service delivery.
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.