Chatbot-Guided ML Pipeline Composition for Schema and QoS Adaptation
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Solution Overview
Problem
Existing machine learning applications require significant programming knowledge, lack interoperability, and struggle with data schema reconciliation, performance adaptation, and manual intervention in model composition, leading to inefficiencies and suboptimal performance.
Innovation Solution
A machine learning platform with a chatbot interface that generates a library of components, allowing users to create applications without coding expertise, utilizing self-adjusting features for data schema mapping, adaptive pipelining, and ontology-based service composition to optimize model performance and deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning tools are used to generate models, then model performance on classification accuracy is improved, but the system lacks adaptability to runtime data changes and QoS violations
Solution Approach 1:
The system implements dynamic model composition where pipelines can be automatically adjusted at runtime based on data quality metrics and QoS conditions. The adaptive pipeline composer modifies model components and data processing steps dynamically in response to changing conditions, allowing the system to maintain accuracy while adapting to runtime variations.
Solution Approach 2:
The system incorporates feedback mechanisms through monitoring engines that continuously track data quality and model performance metrics. This feedback loops back to the pipeline composer, which automatically adjusts the model composition based on observed performance degradation or QoS violations, creating a closed-loop adaptive system.
2Measurement precision
If manual programming is used to build custom machine learning models, then model performance can be optimized, but the complexity and time required increase significantly
Solution Approach 1:
The system segments model building into reusable components and templates stored in a library. Instead of programming entire models from scratch, users compose models by selecting and combining pre-defined components, reducing complexity while maintaining performance through careful component selection.
Solution Approach 2:
The system introduces an intermediary layer of abstraction through standardized interfaces and component libraries. This intermediary allows users to build models using high-level abstractions rather than low-level programming details, reducing complexity while preserving the ability to optimize performance through component configuration.
3Adaptability or versatility
If customizable data schemas are used to store client data, then data flexibility is improved, but schema reconciliation complexity increases
Solution Approach 1:
The system introduces standardized data schemas as intermediaries between diverse data sources and machine learning models. Customizable client schemas are automatically mapped to standard schemas through reconciliation processes, which then interface with model components. This intermediary layer abstracts away schema differences and reduces reconciliation complexity.
Solution Approach 2:
The system performs preliminary schema mapping and transformation before data reaches the model processing stage. By pre-reconciling schemas and preparing data in advance, the system reduces the complexity of runtime reconciliation operations and enables more efficient data processing.
4Measurement precision
If existing machine learning tools compose pipelines based on model performance, then classification accuracy is maximized, but the system cannot adapt to QoS violations or online data metric changes
Solution Approach 1:
The system implements dynamic pipeline composition where the model architecture and data processing steps can automatically reconfigure in response to QoS conditions. When QoS violations or data quality degradation are detected, the adaptive pipeline composer dynamically adjusts the pipeline to maintain reliable operation while preserving accuracy through monitored and validated components.
Data Source
AI summary
The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.