Multi-Cloud Machine Learning Model Build System
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Solution Overview
Problem
The development of Machine-Learning Models (MLMs) is hindered by manual Command Line Interface (CLI) processes, leading to lengthy development times, scalability issues, and increased vulnerability to human errors due to frequent updates, especially in dynamic retail environments. Additionally, MLMs are often limited to single Cloud Service Providers (CSPs), making deployment across multiple CSPs cumbersome and costly.
Innovation Solution
A system and method for multi-cloud MLM building that provides a user-friendly CLI to automatically deploy MLMs across multiple CSPs, utilizing I/O managers, adapters, and listeners to translate input data into cloud-specific instructions, process APIs, and stream feedback, enabling simultaneous deployment and real-time error detection across different CSP environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual CLI processes are used for MLM development, then developers can control each development step, but the development process becomes lengthy and unscalable
Solution Approach 1:
The patent introduces an intermediary build system that sits between the developer and multiple cloud providers. This build system automatically translates a single build configuration into cloud-specific instructions for multiple CSPs, eliminating the need for manual repetition while maintaining controlled development processes.
Solution Approach 2:
The build system is designed to be universal across multiple cloud service providers. A single build configuration can be deployed to multiple different cloud environments (AWS, Azure, GCP, etc.) simultaneously, making the development process scalable and multi-functional without requiring separate manual processes for each cloud provider.
2Adaptability or versatility
If manual build steps are repeated for each tenant, then each tenant gets a customized build, but the process becomes unscalable and time-consuming
Solution Approach 1:
The build system performs preliminary actions by pre-configuring build templates and parameters that can be automatically instantiated for multiple tenants. Instead of manually building for each tenant, the system prepares reusable build configurations that can be rapidly deployed across multiple tenants simultaneously, reducing time while maintaining customization.
Solution Approach 2:
The system creates and manages copies of build configurations for different tenants. A single validated build can be copied and deployed to multiple tenants with minimal modification, allowing rapid scaling while maintaining tenant-specific requirements through parameterization rather than complete manual recreation.
3Productivity
If frequent model updates are implemented, then the MLM stays current with dynamic requirements, but version tracking becomes vulnerable to human errors
Solution Approach 1:
The build system implements automated feedback mechanisms that track model versions, build parameters, and deployment status across all tenants. This automated tracking and version control system eliminates manual version management errors while supporting frequent updates, providing reliable audit trails and rollback capabilities.
4Reliability
If CSP-specific CLI is used, then deployment works with that specific cloud provider, but deploying to multiple CSPs requires repetitive adjustments
Solution Approach 1:
The build system is designed to be universal across multiple cloud service providers. It maintains reliable deployment to each specific CSP while eliminating the need for repetitive manual adjustments by automatically translating a single build configuration into cloud-specific instructions for multiple CSPs simultaneously.
5Device complexity
If single cloud deployment is used, then the build process is simple, but performance optimization and cost savings through mixed cloud services are unavailable
Solution Approach 1:
The build system provides multi-functionality by supporting both simple single-cloud deployments and complex multi-cloud strategies. Developers can deploy to a single cloud provider for simplicity or simultaneously deploy to multiple different cloud providers for performance optimization and cost management, all through the same unified build interface.
Data Source
AI summary
Machine-Learning Model (MLM) build technique is provided. Build instructions are normalized into a cloud-independent format. Each cloud identified in the instructions is assigned a specific adapter. The adapter translates the normalized format into a cloud-specific format and the adapters interact with configuration Application Programming Interfaces (APIs) of the specific cloud to process the instructions in the cloud-specific format. As the adapters interact with the corresponding APIs, output feedback from the APIs is live streamed within an interface to a developer that provided the instructions for monitoring the builds simultaneously being configured on multiple clouds. When the build instructions complete with the APIs on the clouds, the user is presented an option to initiate an orchestrator that loads, initiates, and trains the MLM as a unique instance of the MLM on each cloud.