Machine Learning Component Library for Automated Model Composition
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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 efficient reuse of models, leading to inefficiencies and suboptimal performance.
Innovation Solution
A machine learning platform that generates a library of components, uses a chatbot for intuitive interface, and employs self-adjusting features to create applications without extensive programming, adapts to data changes, and optimizes model performance using data ontologies and adaptive pipelining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning tools are used to generate models, then model performance can be optimized, but programming knowledge and manual effort are required
Solution Approach 1:
The system performs automatic model composition by having the system itself select and combine ML components based on performance metrics and QoS constraints, eliminating the need for data scientists to manually program model architectures. The automated service composition engine autonomously generates pipelines by evaluating model performances on training data and assembling optimal configurations.
Solution Approach 2:
An automated service composition engine acts as an intermediary between raw ML components and final deployed models. This intermediary layer handles the complex task of selecting, configuring, and combining multiple ML models and data processing steps, translating high-level performance requirements into concrete model architectures without requiring user programming expertise.
2Reliability
If manual data reconciliation processes are used, then data schema compatibility can be achieved, but time and effort are consumed
Solution Approach 1:
The system performs preliminary data reconciliation by automatically detecting schema differences between client data and standardized ML model requirements before model training begins. The automated service composition engine identifies and transforms incompatible data schemas in advance, preparing reconciled data pipelines that can be directly consumed by ML models without manual intervention during deployment.
3Measurement precision
If existing machine learning frameworks are used, then classification accuracy can be maximized, but adaptability to changing data conditions is limited
Solution Approach 1:
The system implements dynamic model adaptation by continuously monitoring data distribution changes and automatically re-composing ML pipelines in response to drifting data conditions. The automated service composition engine can dynamically select different model configurations, features, and preprocessing steps based on current data characteristics, allowing the system to adapt to changing conditions while maintaining high classification accuracy.
Solution Approach 2:
The system changes model parameters and architecture configurations based on observed data patterns and performance metrics. The automated composition engine adjusts hyperparameters, selects different algorithm types, and modifies pipeline structures in response to changing data conditions, enabling the system to maintain optimal performance across diverse and evolving data scenarios.
4Measurement precision
If custom machine learning models are built for each application, then specific problem performance can be optimized, but reuse of models is difficult
Solution Approach 1:
The system segments ML applications into reusable atomic components and building blocks that can be independently selected and combined. The automated service composition engine breaks down complex models into modular units such as feature extractors, classifiers, and post-processing steps, allowing these segments to be reused across different applications by re-composing them in new configurations tailored to specific problems.
Solution Approach 2:
The system creates universal ML components that can serve multiple functions across different applications. The automated composition engine identifies common patterns and reusable elements from training data that can be applied to various client problems, enabling a single set of base components to be universally deployed across multiple domains through automatic pipeline composition rather than building custom models for each application.
Data Source
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AI summary
The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.