AI Operating System for Automated ML Pipeline Construction
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Solution Overview
Problem
Current AI and ML development processes are hindered by complex, manual, and time-consuming workflows, requiring extensive programming skills and expertise, with a lack of scalability, interoperability, and governance, leading to high operational costs and delays in model deployment and adaptation to dynamic changes.
Innovation Solution
A data science workflow framework with an AI operating system (OS) that simplifies the construction of AI/ML pipelines through a graphical user interface, enabling user-friendly, automated, and scalable construction of reproducible, interoperable AI/ML models with governance, using a software development kit (SDK) for end-to-end management of the AI/ML application lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processes and customized scripts are used to assemble AI applications, then flexibility in crafting solutions is maintained, but device complexity and operational costs increase significantly
Solution Approach 1:
The patent segments the AI application assembly process into standardized blocks representing different AI algorithms, data sources, and processing operations. These blocks can be independently selected, combined, and configured through a graphical interface, reducing the complexity of integrating heterogeneous technologies while maintaining solution flexibility.
Solution Approach 2:
The patent introduces a graphical user interface as an intermediary between users and the complex underlying AI infrastructure. This interface mediates the interaction by providing visual, drag-and-drop assembly of AI applications, eliminating the need for users to directly manage complex scripting and integration details.
2Adaptability or versatility
If manual processes are used for AI model development, then customization capability is preserved, but productivity and time to deployment decrease
Solution Approach 1:
The patent pre-defines standardized blocks for common AI operations such as data loading, model training, evaluation, and deployment. These pre-configured blocks eliminate the need for manual scripting of routine operations, significantly accelerating deployment speed while allowing customization through block combination and configuration.
Solution Approach 2:
The patent enables copying and reusing of standardized AI blocks and configurations across different projects. Users can template new AI applications by copying existing block configurations, reducing redundant manual work and accelerating the development cycle while maintaining customization through selective block modification.
3Manufacturing precision
If extensive programming skills are required for AI development, then precision in model implementation is maintained, but ease of operation decreases
Solution Approach 1:
The graphical user interface serves as an intermediary that translates user-friendly visual selections into precise model implementation configurations. Users can assemble AI applications through intuitive drag-and-drop operations without needing programming skills, while the system automatically generates the precise code and configurations required for accurate model implementation.
Solution Approach 2:
The system provides self-service capabilities by automatically generating, managing, and optimizing the underlying code and infrastructure based on user selections in the graphical interface. This eliminates the need for users to manually write or debug code while maintaining implementation precision through automated code generation and system optimization.
4Adaptability or versatility
If piecemeal assembly of AI components is performed, then adaptability to specific needs is maintained, but loss of time in integration increases
Solution Approach 1:
The patent divides AI applications into standardized, pre-tested blocks that can be independently selected and combined. This segmentation allows users to quickly assemble customized solutions by selecting relevant blocks rather than performing piecemeal assembly from scratch, significantly reducing integration time while maintaining adaptability through flexible block combination.
Solution Approach 2:
The patent performs preliminary actions by pre-assembling and pre-testing AI components as standardized blocks before they are needed in actual applications. This eliminates the time-consuming process of piecemeal assembly and installation during development, as blocks are ready to be directly integrated into final solutions.
Data Source
AI summary
An operating system (OS) and methods via a software development kit for constructing and managing the full artificial intelligence (AI) or machine learning (ML) product development lifecycle. Embodiments of the present disclosure provide for an integrated computing environment comprising one or more software components call blocks, each pre-loaded with an AI OS intelligent functionality. In accordance with certain aspects of the present disclosure, blocks may be linked in a sequential, parallel, or complex topology to form a pipeline for enabling user-friendly data science experimentation, exploration, analytic model execution, prototyping, pipeline construction, and deployment using a GUI. The OS may incorporate an execution engine for constructing and/orchestrating the execution of a pipeline enabling automatic provisioning of optimal computing resources.