AI Application Logger Plug-In for Lifecycle Performance Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing log management tools lack the ability to track various AI application parameters required for performance analysis, making it difficult for developers and data scientists to understand model accuracy, system latency, and decision-making flow, especially with the rapid expansion of libraries and tools across vendors and open-source communities.
Innovation Solution
A system and method that uses fine-tuned large language models (LLMs) to generate a logger plug-in that automatically instruments AI applications, logging and tracking performance metrics, parameters, and execution flow, reducing the need for manual code instrumentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional log analysis tools are used to inspect logs, then manual query-level matching and rule-based policies can be applied, but it becomes impractical to analyze the huge volume of logs generated by modern software systems
Solution Approach 1:
The patent introduces log intelligence algorithms as an intermediary between traditional log analysis tools and the huge volume of logs. These algorithms, powered by machine learning, automatically detect patterns and anomalies in logs, serving as a mediator that transforms the impractical manual inspection process into an efficient automated analysis system.
Solution Approach 2:
The patent replaces the mechanical manual query-level matching and rule-based policies with machine learning-based log intelligence algorithms. This substitution transforms the manual, time-consuming log inspection process into an automated system that can handle huge volumes of logs efficiently.
2Adaptability or versatility
If existing log management tools are used, then a centralized repository for log data can be created, but they lack the ability to log various AI application parameters required for performance tracking
Solution Approach 1:
The patent implements a dynamic logging configuration system that can adapt to different AI application parameters based on user input. The system dynamically determines which parameters to log (such as model accuracy, system latency, inference cost) based on the specific AI application being monitored, making the logging capability flexible and adaptable to various tracking needs.
Solution Approach 2:
The patent segments the logging functionality into specific AI-related parameter tracking modules. Instead of a generic logging system, it creates specialized logging capabilities for different AI parameters (model accuracy, system latency, inference cost, decision-making flow), allowing targeted collection of specific AI application performance data.
3Adaptability or versatility
If libraries and tools are rapidly expanded across vendors and open-source community, then new features and capabilities are added, but breaking changes with new versions make it difficult to analyze performance
Solution Approach 1:
The patent creates a universal logging framework that works across multiple AI libraries and tools from different vendors. The system is designed to handle various AI libraries (such as Hugging Face Transformers, PyTorch, TensorFlow) through a unified interface, making it versatile and compatible with different libraries while maintaining consistent performance tracking capabilities.
Solution Approach 2:
The patent implements a feedback mechanism where the logging system continuously monitors and adapts to changes in library versions and breaking changes. By automatically detecting and adapting to version changes, the system maintains reliable performance analysis consistency even as libraries evolve rapidly.
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
This disclosure relates generally to system and method to track performance of an AI application during development and production lifecycle. Libraries and tools that are used to develop an enterprise AI application are rapidly expanding across vendors and open-source community. These libraries are coming up with new features and breaking changes with new versions making it difficult to application developer and enterprise runtime executor. The method of the present disclosure receives an enterprise artificial intelligence (AI) application and corresponding software environment as input generate a logger plug-in using one or more fine-tuned large language models (LLMs) based on the plurality of instructions provided by the one or more instructors. Additionally, the logger plug-in is utilized to validate correctness of errors and then the logger plug-in is executed with the enterprise AI application to log and track performance of the enterprise AI application.