Workflow Authoring Tool for Machine Learning Pipelines
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Solution Overview
Problem
Current machine learning systems face challenges in efficiently managing big data workflows, requiring significant manual effort and computational resources, and lack reproducibility and collaboration tools for developers and analysts.
Innovation Solution
A machine learning system with an experiment management engine, workflow authoring tool, and execution engine that enables automated dependency handling, collaborative authorship, and reproducible workflows, using data processing operators and schemas to manage and execute machine learning pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual methods are used for building and evaluating machine learning models, then flexibility in model experimentation is maintained, but significant time and manual effort are required
Solution Approach 1:
The system enables self-service automation where the workflow execution engine automatically manages model training, validation, and evaluation processes without requiring manual intervention. The system self-manages dependencies between data processing operators, automatically schedules executions, and handles resource allocation, thereby reducing both manual effort and experimentation time while maintaining full flexibility in model experimentation through the visual workflow authoring interface.
2Ease of manufacture
If traditional data processing applications are used for big data, then simplicity of implementation is maintained, but adequacy in handling large or complex datasets is insufficient
Solution Approach 1:
The system segments big data processing into modular data processing operators that can be independently configured, executed, and managed. Each operator handles specific tasks (data ingestion, preprocessing, model training, validation, evaluation) and can be chained together in workflows. This segmentation maintains implementation simplicity through standardized operator interfaces while achieving reliability in handling large or complex datasets through distributed execution and specialized processing capabilities for each operator type.
3Adaptability or versatility
If machine learning models are continuously updated to meet evolving requirements, then adaptability to new requirements is improved, but reproducibility of experiments decreases
Solution Approach 1:
The system creates detailed copies of entire machine learning workflows, including all data processing operators, their configurations, dependencies, and execution parameters. These workflow definitions can be stored, versioned, and reused to exactly reproduce experiments. When models need to be updated to meet new requirements, the system copies the original workflow definition, modifies it as needed, and executes the copied version, ensuring that both the original and updated experiments are reproducible and can be compared systematically.
4Ease of operation
If manual experimentation processes are used, then individual developer control is maintained, but collaboration efficiency among developers and analysts is reduced
Solution Approach 1:
The system implements a universal workflow authoring and execution platform that serves multiple functions: visual workflow design, automated execution, collaboration tools, version control, and result management. This multi-functional platform enables collaboration among developers and analysts through shared workflow definitions, commenting, and joint experimentation capabilities, while the standardized universal interface keeps the system structure manageable despite its comprehensive functionality.
Data Source
AI summary
Some embodiments include a workflow authoring tool that accesses a text string representation of a workflow and a text string representation of at least a data processing operator type. The workflow authoring tool enables definition of one or more data processing operator types that can be referenced in defining the machine learning workflow. When scheduling a workflow, the text string representation of the workflow can be parsed and traversed to generate an interdependency graph of one or more data processing operators. The text string representation of the data processing operator type can identify operator attributes associated with the data processing operator type.