Declarative Debriefing for Predictive Pipeline Automation

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Solution Overview

Problem

The process of selecting and configuring debriefing components for machine learning model pipelines is manual, error-prone, and exhaustive, particularly for non-data scientists, as it requires choosing the appropriate types of debriefing information and processing operations, which is complex and time-consuming.

Innovation Solution

A framework that automates the selection of debriefing components and auto-completes the machine learning model pipeline by identifying and incorporating processing for generating debriefing outputs, using a catalog of operations and metadata to determine the most appropriate debrief processor nodes based on the predictive algorithm selected.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual selection and configuration of debriefing components is performed, then flexibility and customization are improved, but complexity and time consumption increase significantly

Engineering Contradiction:

Improvecustomization of debriefing componentsVSAvoidcomplexity of pipeline configuration

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically selecting and configuring debriefing components based on the predictive algorithm type. The framework inspects the algorithm node and autonomously determines which debriefing components (e.g., accuracy metrics, confusion matrices, ROC curves) are appropriate, eliminating the need for manual configuration while maintaining adaptability to different algorithm types.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The framework performs preliminary action by pre-defining mappings between predictive algorithm types and their corresponding debriefing components. This preliminary configuration allows the system to automatically retrieve and apply the correct debriefing setup when a predictive algorithm is selected, avoiding complex manual configuration steps.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If manual configuration of debriefing processing is performed, then precision in selecting appropriate metrics is improved, but time consumption and error rate increase

Engineering Contradiction:

Improveprecision of debriefing metrics selectionVSAvoidtime for configuring debriefing

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system automatically determines the precise debriefing metrics needed by inspecting the predictive algorithm type and autonomously selecting appropriate components such as accuracy, precision, recall, F1-score, confusion matrices, or ROC curves based on the algorithm's capabilities and the problem type.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Inventive Principle:

Principle #10Preliminary action

3Reliability

If comprehensive debriefing information is generated, then evaluation accuracy is improved, but processing complexity increases

Engineering Contradiction:

Improveevaluation accuracy of predictive modelVSAvoidcomplexity of debriefing processing

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The framework segments the debriefing process into distinct, manageable components such as accuracy metrics, confusion matrices, ROC curves, and variable importance analysis. Each component is independently configured and processed based on the predictive algorithm type, making the overall complex evaluation process more manageable and systematic.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The framework creates a universal debriefing system that can handle multiple types of predictive algorithms (classification, regression, clustering) through a common architecture. The same framework infrastructure supports different algorithm types by automatically selecting appropriate debriefing components for each, reducing overall system complexity while maintaining comprehensive evaluation capabilities.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11823073B2Declarative debriefing for predictive pipeline

Publication Date: 2023.11.21 SAP SE

AI summary

Provided are systems and methods for auto-completing debriefing processing for a machine learning model pipeline based on a type of predictive algorithm. In one example, the method may include one or more of building a machine learning model pipeline via a user interface, detecting, via the user interface, a selection associated with a predictive algorithm included within the machine learning model pipeline, in response to the selection, identifying debriefing components for the predictive algorithm based on a type of the predictive algorithm from among a plurality of types of predictive algorithms, and automatically incorporating processing for the debriefing components within the machine learning model pipeline such that values of the debriefing components are generated during training of the predictive algorithm within the machine learning model pipeline.