Self-Adaptive ML Platform for QoS Optimization and Schema Alignment
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Solution Overview
Problem
Existing machine-learning applications require significant programming knowledge, lack interoperability, and do not efficiently reuse model components, leading to manual reconciliation processes and inadequate self-adaptive architectures that fail to predict and mitigate changes in system environments, data corruption, and concept drift.
Innovation Solution
A machine-learning platform that generates a library of components, allowing users to create applications without detailed knowledge of cloud infrastructure, analyzes data to select appropriate components, and monitors performance to adjust models, providing remedial measures for changes and optimizing Quality of Service (QoS) dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing machine-learning applications are used, then programming knowledge is required to build custom models, but this increases the complexity and difficulty of operation
Solution Approach 1:
The system enables self-service by allowing users to build machine-learning applications through intuitive interfaces without requiring programming knowledge. The platform automatically generates applications from high-level specifications, performing the complex coding and configuration tasks autonomously.
Solution Approach 2:
The system introduces an intermediary layer between users and the complex machine-learning infrastructure. This intermediary translates user-friendly specifications into executable models, hiding the underlying complexity while maintaining functionality.
2Adaptability or versatility
If machine-learning models are developed with customized schemas, then data storage flexibility is improved, but this creates incompatibility with standardized models requiring manual reconciliation
Solution Approach 1:
The system performs preliminary schema alignment by automatically transforming data to match standardized model schemas before the machine-learning process begins. This pre-processing eliminates the need for manual reconciliation during model execution.
Solution Approach 2:
The system incorporates feedback mechanisms that automatically detect schema mismatches and adjust data transformations in real-time, ensuring compatibility between customized data sources and standardized models without manual intervention.
3Reliability
If machine-learning applications monitor and adapt to changes, then reliability is improved, but this requires complex self-adaptive architectures
Solution Approach 1:
The system implements self-service monitoring where the machine-learning application automatically detects environmental changes, data quality issues, and performance degradation, then executes remedial actions without external intervention.
Solution Approach 2:
The system employs continuous feedback loops that monitor model performance and environmental conditions, automatically triggering adjustments or alerts when thresholds are breached, maintaining reliability through automated response.
4Reliability
If manual reconciliation processes are used for data, then data compatibility is ensured, but this increases the time and labor required
Solution Approach 1:
The system replaces manual reconciliation processes with automated mechanical systems that continuously transform and validate data, ensuring compatibility with standardized schemas without human intervention and significantly improving processing efficiency.
Data Source
AI summary
A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.