Predictive Engine Replay System for ML Model Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning systems face challenges in deploying predictive engines in production environments due to issues with data integration, scalability, real-time deployment, and the difficulty in identifying causes of inaccurate prediction results, as well as tracking sequences of events that trigger specific prediction outcomes.
Innovation Solution
The development of methods and systems for tracking the deployment of predictive engines, allowing for the replay of their performance, which includes deploying engine variants based on parameter sets, receiving queries and actual results, associating them with replay tags, and recording these interactions for later analysis, enabling efficient evaluation and tuning of predictive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive engines are deployed in production environments using existing machine learning systems, then prediction capabilities are provided, but it becomes difficult to identify causes of inaccurate prediction results and track sequences of events triggering specific prediction outcomes
Solution Approach 1:
The system performs preliminary actions by recording the complete state of the predictive engine (including model parameters, data sources, and configuration) before generating predictions. This pre-recording of engine state enables later replay and analysis of prediction sequences, allowing developers to trace back the causes of inaccurate predictions by examining the exact conditions that existed when each prediction was made.
Solution Approach 2:
The system creates copies of the predictive engine's execution state and prediction sequences for later analysis. By capturing and storing replay data that replicates the original prediction environment, the system enables developers to analyze prediction accuracy issues without affecting the live production system, making it easier to identify and diagnose problems.
2Productivity
If multiple engine variants are deployed for evaluation and tuning, then better prediction models can be developed, but the complexity of managing and comparing different engine configurations increases
Solution Approach 1:
The system introduces an intermediary replay mechanism that mediates between multiple engine variants and their evaluation processes. By standardizing how engine states are captured and replayed, the system provides a common framework for comparing different variants, reducing the complexity of managing multiple configurations while maintaining efficient evaluation capabilities.
Solution Approach 2:
The replay system serves multiple functions: it records engine states, stores prediction sequences, enables debugging, and supports comparison of different engine variants. This universal replay mechanism handles all evaluation and tuning activities through a single standardized interface, simplifying the management of multiple engine configurations while maintaining high productivity.
3Measurement precision
If detailed tracking of prediction sequences and engine states is implemented, then better insights into prediction accuracy are gained, but the data storage and processing requirements increase
Solution Approach 1:
The system extracts only the essential elements needed for prediction sequence tracking and engine state recording, separating them from unnecessary data. By selectively capturing relevant parameters, model states, and prediction outcomes while excluding redundant information, the system achieves high measurement precision for prediction performance while minimizing data storage requirements.
Data Source
AI summary
In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing predictive engine evaluation and replay of engine performance. An exemplary system may include, for example: means selecting a first set of one or more algorithms for a machine learning model; tuning a first group of predictive engine parameters for the machine learning model; training the machine learning model with one or more sources of data using the selected first set of one or more algorithms and the first group of tuned predictive engine parameters to generate a first predictive engine variant from the trained machine learning model; selecting a second set of one or more algorithms for a machine learning model which are different than the first set; tuning a second group of predictive engine parameters for the machine learning model which are different than the first group; training the machine learning model with the one or more sources of data using the selected second set of one or more algorithms and the second group of tuned predictive engine parameters to generate a second predictive engine variant from the trained machine learning model; performing multiple experiments using the first and second predictive engine variants; comparing results from the multiple experiments; and deploying either the first predictive engine variant or the second predictive engine variant based on the comparison of the results of the multiple experiments. Other related embodiments are disclosed.