Hybrid Model Serving for Demand-Spike ML Inference
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Solution Overview
Problem
Machine learning techniques are difficult to maintain and manage due to the challenge of obtaining and managing significant computing resources, which can hinder their adoption.
Innovation Solution
A serverless compute architecture is utilized to dynamically provision computing resources for machine learning models, allowing for flexible resource allocation and integration with both server-based and serverless configurations to handle varying demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If significant computing resources are allocated for machine learning techniques, then machine learning model performance and capability are improved, but resource management complexity and difficulty increase
Solution Approach 1:
The patent introduces a serverless compute architecture that acts as an intermediary layer between the machine learning models and the underlying computing infrastructure. This architecture includes a compute service that automatically manages resource allocation, provisioning, and scaling, eliminating the need for users to directly manage complex computing resources while still providing sufficient performance for machine learning workloads.
2Reliability
If dedicated server instances are used for hosting machine learning models, then model access and inference capability are ensured, but capacity utilization efficiency decreases during demand surges
Solution Approach 1:
The patent implements a dynamic resource allocation system where the compute service automatically adjusts computing capacity based on real-time demand conditions. During demand surges, the system dynamically provisions additional compute resources to handle the increased load, and during low-demand periods, it scales down resources to optimize cost efficiency, thereby maintaining both reliability and high capacity utilization.
Solution Approach 2:
The serverless compute architecture enables a single computing infrastructure to serve multiple machine learning models and multiple clients simultaneously. The compute service manages a pool of computing resources that can be dynamically allocated to different models based on demand, allowing the same infrastructure to fulfill diverse inference requests efficiently without requiring dedicated servers for each model.
3Productivity
If computing resources are dynamically provisioned using serverless architecture, then resource utilization efficiency and scalability are improved, but infrastructure complexity increases
Solution Approach 1:
The patent implements a self-service compute service that automatically performs resource provisioning, scaling, and management without requiring manual intervention or complex infrastructure configuration by users. The system includes automated mechanisms for monitoring demand, allocating computing resources, managing model deployment, and optimizing resource utilization, thereby achieving high efficiency while abstracting away infrastructure complexity from the end users.
Data Source
AI summary
A system is configured to provide access to a machine learning model by using a hybrid configuration comprising a dedicate server on which an instance of a model server is installed, and a serverless compute architecture that interfaces with an instance of the model server using an extension. A first portion of requests directed to the model server are processed by the dedicated server, and a second portion of the requests is processed by the serverless compute architecture.