No-Code Onboarding Platform for Machine Learning Model Deployment
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Solution Overview
Problem
Existing machine learning model onboarding techniques require significant effort, are error-prone, and have inconsistent interfaces and data formats, especially when deploying to multiple environments, necessitating custom solutions that are time-consuming and inefficient.
Innovation Solution
A machine-learning model-agnostic cloud-agnostic no-code onboarding platform (MAOW) with a universal unified interface, enabling automatic no-code configuration and deployment of machine learning models across various environments without requiring manual coding, using a visual application system and a directed acyclic graph for model workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom onboarding solutions are used for each target environment, then the model can be deployed to specific environments, but the process becomes time-consuming and requires significant effort
Solution Approach 1:
The patent implements a universal onboarding interface that works across multiple target environments (cloud platforms, on-premises systems, data centers) without requiring custom solutions for each environment. The system uses environment-agnostic abstractions and standardized protocols to enable a single onboarding process to serve multiple deployment targets, thereby reducing onboarding time while maintaining adaptability.
Solution Approach 2:
The patent introduces an intermediary layer between the machine learning model and the target environment, consisting of standardized adapters and translators. This intermediary handles the complexity of environment-specific integrations, allowing the model to be onboarded once and then deployed to multiple environments through the mediator's translation capabilities.
2Reliability
If custom onboarding solutions are developed for each environment, then specific environment requirements can be met, but the process becomes error-prone and inconsistent
Solution Approach 1:
The patent establishes a homogeneous onboarding interface with standardized data formats, protocols, and workflows that are consistent across all target environments. Instead of having different onboarding processes for each environment, the system uses uniform interfaces that translate to environment-specific implementations internally, ensuring consistency and reducing errors.
Solution Approach 2:
The patent segments the onboarding process into distinct, modular components: universal interface layer, environment-specific adapters, and deployment orchestrator. This segmentation allows each component to be independently validated and tested, improving reliability while reducing overall complexity through modularity.
3Ease of manufacture
If manual coding is required for onboarding, then specific customization can be achieved, but the process becomes labor-intensive and less efficient
Solution Approach 1:
The patent implements self-service onboarding capabilities where the system automatically detects target environments, generates appropriate deployment configurations, and executes the onboarding process without requiring extensive manual coding. The system uses automated tools to handle environment setup, data format translation, and deployment orchestration, significantly improving productivity while maintaining ease of use.
Solution Approach 2:
The patent performs preliminary actions by pre-configuring environment adapters, pre-validating deployment templates, and pre-establishing communication protocols before the actual onboarding process. This preparation enables faster, more efficient onboarding with less manual intervention required during the deployment phase.
Data Source
AI summary
As described herein, a system, method, and computer program are provided for a machine-learning model-agnostic cloud-agnostic no-code onboarding platform. In use, a universal unified interface to a Model-Agnostic Onboarding Workflow (MAOW) platform is provided, wherein the MAOW platform is configured as a no-code, model-agnostic, cloud-agnostic platform. Additionally, at least one machine learning model is onboarded using the MAOW platform, based on the universal unified interface. Further, the at least one machine learning model is deployed from the MAOW platform to at least one target environment.