Machine Learning Model Metrics Framework
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Solution Overview
Problem
In large business enterprises, monitoring and maintaining multiple machine learning models with different data formats and schema is cumbersome, requiring manual setup and leading to computational inefficiencies and difficulties in integrating data sources and generating standardized metrics.
Innovation Solution
A model monitoring service that automatically generates and processes metrics for machine learning models by normalizing data, inferring data types, and segmenting metrics into buckets, allowing for customizable and standardized metric calculation across diverse models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual setup and monitoring is used for each machine learning model, then customization and control are improved, but device complexity and time consumption increase significantly
Solution Approach 1:
The patent implements a universal monitoring service that can handle multiple machine learning models with different data formats and schemas through a single standardized interface. The system normalizes diverse model outputs into a common format, allowing one monitoring service to perform the function of multiple individual monitoring setups, thereby reducing overall system complexity while maintaining operational control.
Solution Approach 2:
The patent introduces a data normalization layer as an intermediary between diverse machine learning model outputs and the monitoring service. This normalization layer translates various data formats and schemas into a standardized intermediate format, enabling the monitoring service to process multiple model types without direct complex interactions, thus reducing system complexity.
2Measurement precision
If manual coding is required for each model monitoring setup, then measurement precision is improved, but loss of time and productivity decrease
Solution Approach 1:
The patent implements preliminary data normalization and schema standardization processes that prepare model outputs in advance for monitoring. By pre-defining standardized data formats and normalization rules, the system eliminates the need for manual coding during metric generation, maintaining measurement precision through standardized processes while significantly improving setup efficiency.
Solution Approach 2:
The patent uses template-based metric generation that copies and adapts standardized monitoring patterns across different models. Instead of manually coding each monitoring setup, the system replicates proven monitoring templates and adjusts them through configuration rather than programming, preserving metric accuracy while dramatically reducing setup time.
3Adaptability or versatility
If data normalization is implemented across diverse models, then adaptability is improved, but computational overhead and device complexity increase
Solution Approach 1:
The patent segments the data normalization process into distinct modular components: schema validation, format conversion, and standardization transformation. Each component handles a specific aspect of normalization independently, making the overall complex process manageable and maintainable while achieving high adaptability across diverse machine learning models.
4Productivity
If standardized metrics are generated for multiple models, then productivity is improved, but measurement precision may be compromised due to format differences
Solution Approach 1:
The patent dynamically adjusts normalization parameters and transformation rules based on the specific data schema and format of each machine learning model. By changing parameters such as data types, scaling factors, and aggregation methods according to model-specific characteristics, the system maintains measurement precision while achieving standardized metric output across diverse models.
Data Source
AI summary
A method including extracting, from an input, supported data. The input includes outputs from machine learning models in different formats. The supported data includes a subset of the input after data normalization. The method also includes inferring, from the supported data, data types to be used with respect to generating metrics for the machine learning models. The method also includes generating, from the supported data and using the data types, a relational event including the supported data. The relational event further includes a first data structure object including the types and having a first data structure different than the different formats. The method also includes calculating, using the supported data in the first data structure, the metrics for the machine learning models. The method also includes generating, from the relational event, a monitoring event. The monitoring event includes a second data structure object segmented into data buckets storing the metrics.