Standardized ML Model Library and API for Enterprise Development
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in efficiently building and managing machine learning models across enterprises, particularly in standardizing data pipelines, integrating inferencing engines, and orchestrating end-to-end machine learning operations.
Innovation Solution
A library of standardized modules and a software development kit (SDK) that includes tools for model developers to build machine learning models, integrated with an Application Programming Interface (API) for seamless module incorporation and tool access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data scientists and software engineers manually build different machine learning models for different projects, then model customization and adaptability are improved, but software engineering time and development complexity increase
Solution Approach 1:
The system segments machine learning model development into reusable modular components stored in a library. These modules can be independently developed, tested, and combined to build different ML models, reducing manual engineering time while maintaining customization capability.
Solution Approach 2:
The patent creates a universal library of ML model components that can serve multiple projects and use cases. These standardized modules provide multi-functional capabilities that can be applied across different enterprise projects, reducing redundant development work.
2Productivity
If enterprises use standardized libraries and SDKs for building machine learning models, then development efficiency and consistency are improved, but flexibility and adaptability to specific project needs may worsen
Solution Approach 1:
The system provides dynamic configurability where standardized modules can be selectively combined and configured based on specific project requirements. The architecture allows teams to start with pre-built standardized components and dynamically adjust or extend functionality as needed for each project.
Solution Approach 2:
The patent implements preliminary action by providing pre-built, standardized ML model components in a library that have been previously developed and validated. These ready-to-use modules accelerate development while maintaining the ability to customize through selective combination and configuration.
3Adaptability or versatility
If a comprehensive library of ML model tools is provided, then model building capabilities are improved, but system complexity and integration difficulty worsen
Solution Approach 1:
The patent introduces an intermediary layer (the standardized library interface and SDK) that simplifies access to complex ML model tools. This intermediary provides a unified, consistent API that abstracts away the underlying complexity of individual modules, making the system easier to integrate and use.
Data Source
AI summary
Building machine learning models using a standardized library of machine learning model tools. An Application Programming Interface (API) serves as an interface between one or more computer applications that receive user commands for building machine learning models and the standardized library of tools. The API provides an interface between data scientists tasked with designing machine learning models conceptually and a standardized set of software engineering tools. The API enables incorporation of the relevant standardized software engineering tools to build the machine learning models that have been designed conceptually by the data scientists.