ML Infrastructure Provisioning via Reusable Templates
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Solution Overview
Problem
Existing serving infrastructures require manual and complex processes to onboard new machine learning applications, involving the design and provisioning of infrastructure such as web servers, continuous integration pipelines, and continuous deployment pipelines, which can take months to complete.
Innovation Solution
The implementation of templates that can be used to automatically provision infrastructures for machine learning applications in a multi-tenant on-demand serving infrastructure, reducing the manual effort required and allowing for quicker provisioning in a matter of hours or days.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual processes are used to design and provision infrastructure for machine learning applications, then infrastructure can be customized to specific needs, but the onboarding process takes months to complete
Solution Approach 1:
The patent applies preliminary action by pre-defining infrastructure templates that include all necessary configurations, components, and settings before they are needed. These templates are prepared in advance with standard configurations, so when a new machine learning application needs to be onboarded, the infrastructure is already structured and ready, eliminating the need for manual design and provisioning from scratch.
Solution Approach 2:
The patent uses copying by creating reusable infrastructure templates that can be replicated for multiple machine learning applications. Instead of manually provisioning infrastructure for each application, the system copies and instantiates pre-defined templates, which significantly reduces onboarding time while maintaining consistency across different deployments.
2Productivity
If manual provisioning processes are used, then infrastructure can be tailored to specific application requirements, but extensive manual effort is required
Solution Approach 1:
The patent implements self-service by enabling the infrastructure provisioning system to automatically execute template definitions without requiring extensive manual intervention. The system self-configures resources, deploys components, and sets up connections based on template specifications, transforming the process from manual operations to automated self-provisioning while maintaining the ability to meet specific application requirements.
3Loss of time
If templates are used to automatically provision infrastructure, then onboarding time is reduced to days, but the system must manage multiple template configurations
Solution Approach 1:
The patent applies universality by designing a template management system that handles multiple infrastructure configurations through a unified framework. The same template engine and management mechanisms work for different infrastructure types, application scenarios, and configuration complexities, reducing the perceived complexity of managing multiple templates while enabling rapid provisioning across diverse use cases.
Data Source
AI summary
A method by one or more electronic devices to provision an infrastructure for a machine learning application in a multi-tenant on-demand serving infrastructure. The method includes storing a plurality of templates, wherein each of the plurality of templates indicates a scoring interface, a web server, a definition of a continuous integration pipeline, and a definition of a continuous deployment pipeline, receiving a request to provision the infrastructure for the machine learning application using a specified template from the plurality of templates, and provisioning the infrastructure for the machine learning application using the specified template to create a version control system repository, a continuous integration pipeline, and a continuous deployment pipeline.