Automated AI Pipeline Framework for Model Deployment
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Solution Overview
Problem
Current AI and ML development processes are hindered by complexity, requiring extensive expertise and manual processes, leading to slow development, high operational costs, and difficulties in scalability, interoperability, and governance, especially in integrating heterogeneous technologies and managing the lifecycle of ML models.
Innovation Solution
A data science workflow framework and AI operating system (OS) that simplifies the construction of AI/ML pipelines through a graphical user interface, automates integration, and provides a comprehensive software development kit (SDK) for streamlined resource management and deployment, enabling non-experts to build and deploy AI solutions efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes and extensive expertise are used for AI/ML development, then model quality and complexity can be maintained, but development speed decreases and operational costs increase
Solution Approach 1:
The patent divides the AI/ML development lifecycle into distinct stages (data preparation, model training, evaluation, deployment) and provides specialized tools for each segment. This segmentation allows automated handling of routine tasks while maintaining expertise for critical decisions, thereby increasing development speed without sacrificing model quality.
Solution Approach 2:
The patent introduces an intermediary platform that bridges the gap between data scientists and infrastructure. This platform provides standardized templates, pre-configured environments, and automated workflows that translate complex manual processes into automated actions, increasing productivity while managing complexity through abstraction.
2Adaptability or versatility
If heterogeneous technologies are integrated manually, then flexibility and adaptability are maintained, but integration complexity increases and scalability is hindered
Solution Approach 1:
The patent provides a universal integration framework that can accommodate multiple heterogeneous technologies through standardized interfaces. This framework acts as a common language that translates between different technologies, enabling flexible integration while reducing complexity through standardization.
Solution Approach 2:
The patent enables dynamic configuration of integration parameters through automated parameter optimization. By allowing systematic variation and selection of parameters across different technologies, the system achieves adaptability while managing complexity through automated parameter management rather than manual configuration.
3Productivity
If automated tools are used for AI/ML development, then development speed and scalability improve, but expertise requirements and governance challenges increase
Solution Approach 1:
The patent provides pre-configured templates, pre-trained models, and pre-established governance frameworks that are prepared in advance. Users can leverage these pre-actions to quickly deploy AI/ML solutions without needing extensive expertise, while automated governance ensures compliance without increasing operational complexity.
Solution Approach 2:
The patent implements self-service capabilities where the system automatically handles routine tasks such as resource provisioning, model monitoring, and governance compliance. This self-service approach increases deployment efficiency while reducing the need for continuous expert intervention, as the system manages itself within established governance boundaries.
4Productivity
If manual model packaging and deployment is performed, then control over the process is maintained, but time consumption and operational costs increase
Solution Approach 1:
The patent implements continuous integration and continuous deployment (CI/CD) pipelines that automatically package and deploy models as they are trained. This eliminates the discontinuous manual packaging process, maintaining consistent deployment speed while reducing the time loss associated with manual intervention through automated continuous workflows.
Data Source
AI summary
A data processing method and system for automated construction, resource provisioning, data processing, feature generation, architecture selection, pipeline configuration, hyperparameter optimization, evaluation, execution, production, and deployment of machine learning models in an artificial intelligence solution development lifecycle. In accordance with various embodiments, a graphical user interface of an end user application is configured to provide a pre-configured template comprises an automated ML framework for data import, data preparation, data transformation, feature generation, algorithms selection, hyperparameters tuning, models training, evaluation, interpretation, and deployment to an end user. A configurable workflow is configured 10 to enable a user to assemble one or more transmissible AI build/products containing one or more pipelines and/or ML models for executing one or more AI solutions. Embodiments of the present disclosure may enable full serialization and versioning of all entities relating to an AI build/product for deployment within an enterprise architecture.