ML Pipeline Logging Adapter for Dev-Production Artifact Sync
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Solution Overview
Problem
Existing machine learning (ML) pipelines face challenges in managing infrastructure and data format differences between development and production environments, leading to disjointed computing systems and difficulties in tracking updates and performance metrics.
Innovation Solution
A unified machine learning pipeline with a logging adapter that synchronizes data and artifacts across development and production environments, using data loggers and adapters to facilitate automatic updates and real-time processing, supporting both batch and real-time data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate ML pipelines are used for development and production environments, then each environment can be optimized independently, but the systems become disjointed and difficult to synchronize
Solution Approach 1:
The patent merges development and production ML pipelines into a unified system where both environments share common infrastructure, data formats, and synchronization mechanisms. This allows independent optimization while maintaining integration through centralized artifact storage and automated synchronization processes.
Solution Approach 2:
The unified pipeline implements universal data formats and infrastructure that serve both development and production environments simultaneously. The system provides multi-functional capabilities including training, validation, and deployment within a single standardized framework that adapts to different environmental requirements.
2Loss of information
If manual tracking of updates and artifacts is implemented, then data can be monitored, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system implements self-service automated tracking where the ML pipeline automatically logs artifacts, tracks updates, and synchronizes data between environments without manual intervention. The automated logging and synchronization processes eliminate manual tracking efforts while ensuring complete artifact monitoring.
Solution Approach 2:
The unified pipeline incorporates feedback mechanisms that automatically monitor and track artifacts, updates, and model performances. The system provides continuous feedback loops that detect changes, trigger synchronization, and maintain accurate records of all ML operations across development and production environments.
3Stability of the object's composition
If data synchronization between environments is performed frequently, then data consistency is improved, but system latency increases
Solution Approach 1:
The system implements periodic synchronization at strategically determined intervals rather than continuous real-time synchronization. The synchronization frequency is optimized to maintain data consistency while minimizing latency impact on production operations.
Solution Approach 2:
The synchronization mechanism dynamically adjusts its frequency and timing based on system conditions, data change rates, and production requirements. The system can increase synchronization frequency when consistency is critical and reduce it during high-latency periods to optimize overall system performance.
Data Source
AI summary
Systems and methods are provided for a machine learning (ML) pipeline with a unified framework. A machine learning pipeline trains a machine learning model in the machine learning pipeline in a development environment to generate training artifacts. The machine learning pipeline further executes the machine learning model in a production environment to generate production artifacts. When a training data logger and the machine learning pipeline are in communication with each other, the machine learning pipeline automatically transmits the training artifacts to the training data logger for storage in the development environment. When the production data logger and the machine learning pipeline are in communication with each other, the machine learning pipeline automatically transmits the production artifacts to the production data logger for storage in the production environment.