Cross-Account Model Deployment with Automated Artifact Transfer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The transfer of validated machine learning models between different teams or accounts is often manual, slow, unreliable, non-repeatable, and expensive, particularly in the domains of data science and software engineering.
Innovation Solution
A computer-implemented method for cross-account model deployment that automates the process using artifact identifiers and account tuples to move models between computing accounts, ensuring reliability, repeatability, and cost-effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual transfer of models between accounts is performed, then flexibility and control are maintained, but the process becomes slow, unreliable, and expensive
Solution Approach 1:
The system performs self-service by automatically moving model artifacts between accounts using pre-configured account tuples and artifact identifiers. The automated deployment system executes transfers without requiring manual human intervention, thereby improving reliability while reducing deployment time through consistent, error-free automated operations.
2Productivity
If automated deployment is implemented, then speed and consistency are improved, but system complexity increases
Solution Approach 1:
The system applies preliminary action by pre-configuring account tuples containing source and target account identifiers before deployment begins. This preparation work is done in advance, allowing the actual deployment to execute quickly and consistently without complex real-time decision-making, thereby improving productivity while managing system complexity through upfront configuration.
Solution Approach 2:
The deployment system is segmented into distinct components: account tuples for authentication, artifact identifiers for model selection, and automated transfer mechanisms. This segmentation allows each component to be independently managed and configured, improving overall deployment speed while reducing system complexity through modular design.
3Reliability
If manual model transfer is used, then security boundaries between teams are maintained, but the process becomes non-repeatable and expensive
Solution Approach 1:
The automated system performs self-service by using pre-configured account tuples to authenticate and execute model transfers. This eliminates the need for expensive manual intervention while ensuring repeatable deployments through consistent automated execution, thereby improving deployment repeatability and reducing costs.
4Reliability
If automated artifact transfer is implemented, then deployment consistency is improved, but the risk of automation errors increases
Solution Approach 1:
The system performs preliminary validation and configuration of account tuples and artifact identifiers before automated transfer begins. By preparing and validating all parameters in advance, the system ensures deployment consistency through standardized automated processes while minimizing automation errors through pre-execution checks and configuration validation.
Data Source
AI summary
A computer-implemented method for cross-account model deployment is provided. According to the method, at least one artifact identifier of at least one to-be-deployed model artifact is provided. Further, at least one account tuple, the account tuple comprising a source account identifier identifying a source account and a target account identifier identifying a target account, is provided. Finally, the at least one to-be-deployed model artifact is moved from the source account to the target account. Further, a computer program element and a computer readable medium are provided.