Compute Deployment Archive Bundle Governance
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Solution Overview
Problem
The deployment of rule-based and model-based computes in production environments is complex, requiring effective governance to ensure quality, compliance, and risk management, while maintaining lineage and traceability of dependencies, which existing methods struggle to automate efficiently.
Innovation Solution
A method that generates a deployable archive bundle of computes with dependencies, allowing for automatic deployment and replacement in production environments based on performance assessments, using preconfigured script paragraph templates and user-defined links to maintain lineage and dependencies, thereby streamlining the deployment process and ensuring self-documentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual deployment processes are used for computes in production environments, then governance and quality control can be maintained, but deployment complexity and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by creating deployable archive bundles that contain pre-packaged computes with all their dependencies, logical building blocks, and lineage information. This pre-packaging allows the compute to be deployed as a complete, self-contained unit, eliminating the need for manual coordination of multiple components during deployment while maintaining governance requirements.
Solution Approach 2:
The deployment system enables self-service by automatically unpacking the deployable archive bundle and deploying the compute with its dependencies in the production environment. The system self-detects what needs to be deployed, self-manages the deployment process, and self-documented the lineage, reducing manual intervention while maintaining quality control through automated governance checks.
2Adaptability or versatility
If computes with dependencies are deployed without automated packaging, then deployment flexibility is maintained, but lineage tracking and traceability become difficult to maintain
Solution Approach 1:
The system merges the compute, its dependencies, logical building blocks, and lineage information into a single deployable archive bundle. This consolidation ensures that all components and their relationships are packaged together, maintaining complete lineage and traceability information within the bundle itself, which can then be deployed flexibly to different production environments.
3Productivity
If performance analysis and replacement of production computes is automated, then productivity improves, but system complexity increases
Solution Approach 1:
The system implements feedback by automatically analyzing the performance of the deployed compute in the production environment, comparing it against expected performance metrics, and triggering replacement actions when performance degradation is detected. This closed-loop feedback mechanism automates the deployment and monitoring process, improving productivity while managing complexity through systematic performance tracking and automated decision-making rules.
Data Source
AI summary
Techniques for automatically deploying a version of a compute, both rule based and model based, with its dependencies when approved for deployment using one or more governance processes. One technique includes generating a compute fitting defined requirements and capable of executing on a defined model objective, generating a deployable archive bundle of the compute with dependencies based on a record of a lineage of the logical building blocks in dependency, analyzing performance of the compute with respect to a production compute deployed within a production environment, determining the performance of the compute is superior to the performance of the production compute based on the analyzing, and replacing the production compute in the production environment with the compute using the deployable archive bundle to facilitate deployment.