ML Pipeline Management for Automated Software Deployment
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Solution Overview
Problem
The deployment of software services in new data centers is a manual, computationally expensive process that requires significant interaction from developers and can lead to downtime, especially when scaling computing capacity in multi-region computing networks.
Innovation Solution
A computing environment that uses machine learning to identify necessary configuration settings for software deployment by analyzing past deployment data across regions, automating the deployment process through a code delivery service, and integrating with host provisioning services to minimize manual intervention and reduce downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual service pipeline deployment is used, then deployment can be performed with existing tools and processes, but the process is computationally expensive and requires significant developer interaction
Solution Approach 1:
The system enables self-service deployment by automatically generating configuration settings for new regions using machine learning models that analyze past deployment data. The service pipeline autonomously identifies required configurations, generates deployment manifests, and executes deployments without manual developer intervention, thereby reducing both computational overhead and human interaction requirements.
Solution Approach 2:
The system performs preliminary actions by pre-generating configuration settings and deployment configurations before actual deployment occurs. Machine learning models analyze historical deployment data in advance to predict and prepare configuration parameters, allowing the deployment process to execute efficiently with minimal real-time computational resources and no manual intervention.
2Productivity
If manual service pipeline deployment is used, then existing deployment processes can be maintained, but deployment time increases and causes periods of downtime
Solution Approach 1:
The automated service pipeline performs self-service deployments by automatically generating configuration settings, validating deployments, and executing service deployments without manual approval steps. This eliminates idle time and waiting periods, continuously advancing the deployment process while minimizing service downtime through efficient resource allocation and parallel processing.
3Ease of operation
If manual configuration settings are defined, then deployment can be performed with existing processes, but significant developer interaction is required
Solution Approach 1:
The system implements self-service by automatically generating configuration settings for service deployments in new regions. The machine learning model analyzes historical deployment data and autonomously produces configuration parameters, eliminating the need for developers to manually define settings while maintaining deployment simplicity through automated pipeline execution.
Solution Approach 2:
The system replaces manual mechanical configuration processes with automated machine learning-based configuration generation. Instead of developers manually setting configuration parameters, the system uses computational models to automatically generate and validate configurations, substituting human manual operations with automated intelligent systems.
4Quantity of substance
If physical hardware is purchased and deployed, then computing capacity can be increased, but the operation becomes incredibly complex
Solution Approach 1:
The system implements a universal service pipeline that can deploy services across multiple regions and cloud environments using a single automated process. The machine learning model and deployment framework are designed to handle diverse hardware configurations and regional requirements through a unified approach, reducing deployment complexity while scaling computing capacity across multiple data centers.
Data Source
AI summary
Machine learning automatic pipeline management for automated software deployment is described. An adjustment to computing capacity for a region of a multi-region computing network is identified. A service to be deployed in the region of the multi-region computing network is further identified. Configuration settings for deployment of the service in the region is generated using past deployment data for the service in other regions of the multi-region computing network. A continuous code delivery service is directed to add a stage to a software deployment pipeline for the region. The stage may be configured using the at least one configuration setting.