Operational Semantics Annotation for Distributed ML Pipelines
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Solution Overview
Problem
Existing solutions struggle to efficiently manage complex multi-step analytics and machine learning pipelines, particularly in distributed network environments, due to challenges in integrating AI and ML technologies with cloud-native environments and ensuring smooth operation in production.
Innovation Solution
A directed acyclic graph (DAG) and data flow graph (DFG) representation of machine learning pipelines are annotated with operational semantics to facilitate efficient management, scaling, and acceleration of these pipelines, using a pipeline manager, processing manager, evaluator, and director to control and schedule operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning pipelines are implemented in distributed network environments, then productivity and scalability are improved, but device complexity and difficulty of management increase
Solution Approach 1:
The patent introduces a pipeline representation system using Data Flow Graphs (DFG) and directed acyclic graphs as an intermediary layer between the complex distributed infrastructure and the user. This intermediary provides standardized abstractions for nodes, edges, and execution semantics, simplifying the management of distributed ML pipelines while maintaining high productivity through efficient resource utilization across the network environment
2Measurement precision
If operational semantics annotations are added to pipeline representations, then control precision and scheduling accuracy are improved, but manufacturing precision requirements increase
Solution Approach 1:
The patent applies parameter changes by introducing a standardized set of operational semantics annotations (input semantics, firing semantics, state semantics, output semantics) that transform the pipeline representation from a simple structural model to a semantically-rich execution model. These annotations provide precise control over node execution without requiring excessive precision in the annotation process itself, as the system handles semantic interpretation and scheduling based on the annotated parameters
3Adaptability or versatility
If selective node annotation with operational semantics is performed, then adaptability and versatility are improved, but device complexity increases
Solution Approach 1:
The patent implements local quality by enabling selective annotation of individual nodes within the pipeline rather than requiring uniform annotation of the entire pipeline. Each node can be annotated with only the specific operational semantics relevant to its function, providing localized control and flexibility while reducing overall system complexity. The processing manager handles these localized annotations efficiently, matching them to appropriate execution behaviors without requiring complex global processing
Data Source
AI summary
A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning pipeline, is represented in a data flow graph (DFG) with nodes and edges. The represented nodes are configured to be annotated with an operational semantic. On order of execution of the pipeline is discovered through the node annotation(s) represented in the annotated DFG, and execution of the pipeline is based on the discovered order. A control signal formatted based on the executed pipeline is configured to dynamically and selectively control an operatively coupled physical hardware device.