AI Model Generation Platform for Hybrid Cloud Deployment
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Solution Overview
Problem
The complexity and time-consuming nature of generating machine learning models, especially when multiple models are required to achieve desired outcomes, poses a challenge in efficiently developing and deploying AI solutions, particularly for non-technical product management teams who struggle to navigate the rapid advancements in AI and ML.
Innovation Solution
A machine learning model generation platform that receives a description of desired operators and generates appropriate machine learning models, utilizing a virtual AI implementer like 'Mentalist' to synthesize solution architectures and a virtual AI orchestrator like 'Matchmaker' to streamline the deployment of AI components and pipelines across hybrid cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine learning models are generated to achieve desired outcomes, then the accuracy and capability of AI solutions are improved, but the complexity and time required for model development increase
Solution Approach 1:
The platform segments the complex task of generating multiple machine learning models into manageable components: automatic model selection, automated training pipelines, and modular deployment. This allows non-technical users to access sophisticated multi-model solutions without being overwhelmed by the underlying complexity.
Solution Approach 2:
The system implements self-service through automated model generation and selection capabilities. The platform automatically selects appropriate models, trains them on provided data, and deploys them without requiring manual intervention from non-technical users, thereby reducing development complexity while maintaining high AI solution capability.
2Reliability
If multiple machine learning models are generated to achieve desired outcomes, then the accuracy and capability of AI solutions are improved, but the time required for model development increases
Solution Approach 1:
The platform performs preliminary actions by pre-configuring model training pipelines, pre-processing data automatically, and pre-selecting appropriate models based on the problem type. This preparation work is done automatically before the user needs the final models, significantly reducing the overall development time while maintaining the ability to generate multiple high-capability models.
Solution Approach 2:
The system maintains continuous useful action through automated parallel processing of multiple models. Instead of sequentially developing each model (which would be time-consuming), the platform continuously and simultaneously trains and evaluates multiple models, optimizing the development process while delivering high-capability AI solutions.
3Ease of operation
If non-technical product management teams use AI/ML platforms, then the accessibility and adoption of AI solutions are improved, but the technical knowledge gap creates challenges in navigating rapid AI advancements
Solution Approach 1:
The platform acts as an intermediary between non-technical users and complex AI/ML technologies. It provides a simplified interface that translates business requirements into technical model specifications, automatically handles data processing and model training, and presents results in business-friendly formats. This intermediary layer shields users from technical complexity while enabling widespread AI adoption.
Data Source
AI summary
A server receives a request including information associated with an objective and generates, using a first machine learning model, an architecture of an artificial intelligence-based solution to address the objective. The server generates the artificial intelligence-based solution based on the architecture by: identifying a second machine learning model in a first database and identifying a third machine learning model in a second database. The second machine learning model is a first portion of the artificial intelligence-based solution and is available via a marketplace. The third machine learning model is a second portion of the artificial intelligence-based solution and is unavailable via the marketplace. The server generates the artificial intelligence-based solution based at least in part on a combination of: the second machine learning model and the third machine learning model. The server enables access to the artificial intelligence-based solution via the marketplace.