Machine Learning Abstraction Layer for Pipeline Management
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Solution Overview
Problem
The complexity of machine learning systems, with multiple interdependent components, makes it difficult to manage and optimize their performance for accurate results, as users must manually determine necessary components and settings, and tracking errors is challenging due to numerous interactions within the system.
Innovation Solution
A machine learning abstraction layer that dynamically selects and adjusts logical groupings of machine learning pipelines based on objectives, using a training pipeline to generate models, inference pipelines for analysis, and a policy pipeline to manage model deployment and settings, with feedback-driven adjustments to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple independent machine learning components are used to deliver accurate results, then the accuracy and relevance of results are improved, but the complexity of managing interdependent aspects increases
Solution Approach 1:
The patent segments the machine learning system into distinct pipeline components (data collection, preprocessing, model training, evaluation, deployment) that can be independently managed yet orchestrated together. This segmentation allows each component to be optimized for accuracy while reducing management complexity through modular structure.
Solution Approach 2:
The patent introduces an intermediary orchestration layer that manages the interactions between multiple machine learning components. This intermediary coordinates data flow, model deployment, and component interactions, reducing the complexity of managing interdependent aspects while maintaining accurate results.
2Adaptability or versatility
If manual determination of components and settings is performed, then customization and control are improved, but the ease of operation deteriorates
Solution Approach 1:
The patent implements dynamic component selection and configuration that adapts to different use cases automatically. The system can dynamically determine which components and settings are appropriate for a given objective, providing customization without requiring manual determination, thus improving ease of operation while maintaining adaptability.
Solution Approach 2:
The patent enables the machine learning system to automatically configure itself by selecting appropriate components and settings based on the objective. The system performs self-service configuration, reducing the need for manual intervention while maintaining customization capabilities through automated decision-making.
3Adaptability or versatility
If numerous components interact within the system, then the functionality and versatility are improved, but the difficulty of detecting and measuring errors increases
Solution Approach 1:
The patent implements comprehensive feedback mechanisms throughout the machine learning pipeline that automatically track and report errors. Each component provides feedback on its operation and interactions, enabling easy detection and measurement of errors despite the numerous interacting components. This feedback system maintains functionality while reducing error tracking difficulty.
Data Source
AI summary
Apparatuses, systems, program products, and method are disclosed for machine learning abstraction. An apparatus includes an objective module configured to receive an objective to be analyzed using machine learning. An apparatus includes a grouping module configured to select a logical grouping of one or more machine learning pipelines to analyze a received objective. An apparatus includes an adjustment module configured to dynamically adjust one or more machine learning settings for a logical grouping of one or more machine learning pipelines based on feedback generated in response to analyzing a received objective.