Unified ML Pipeline With Artifact Adapters for Reliable Model Updates
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Solution Overview
Problem
Existing machine learning (ML) pipelines face challenges in managing the divergence between development and production environments due to differences in data types, access controls, and infrastructure, leading to disjointed computing systems and uncertainty in updating ML models.
Innovation Solution
A cloud computing system with a unified ML pipeline that includes a training data adapter, artifact adapter, and data loggers, enabling automatic data format conversion and model updates across environments, facilitating seamless integration and synchronization of logged data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate ML pipelines are established for development and production environments, then each environment can be optimized independently, but the systems become disjointed and model updates become uncertain
Solution Approach 1:
The patent merges development and production environments into a single unified ML pipeline that can operate in different modes. The pipeline uses a common codebase and shared components, allowing it to function as a training pipeline in development mode and as a serving pipeline in production mode, thereby eliminating system disjointedness while maintaining environment-specific optimizations.
Solution Approach 2:
The unified pipeline employs dynamic configuration through mode indicators that allow the same pipeline to adapt its behavior based on the operational context. The pipeline can dynamically switch between training and serving modes, adjusting its operations accordingly while maintaining a single coherent system structure that ensures reliable model updates.
2Ease of operation
If manual intervention is used for ML model deployment, then control and verification are improved, but automation and efficiency are reduced
Solution Approach 1:
The unified ML pipeline implements self-service capabilities through automated mode switching and self-configuration. The pipeline automatically detects the operational mode through mode indicators and adjusts its behavior accordingly, eliminating the need for manual intervention in deployment processes while maintaining full control over the model lifecycle from training to serving.
3Adaptability or versatility
If data formats differ between training and production, then data specificity is improved, but data conversion and integration become complex
Solution Approach 1:
The unified pipeline uses universal data processing components that can handle multiple data formats through a single interface. The same pipeline code processes both training data and production data by interpreting mode indicators, eliminating the need for separate data conversion pipelines and reducing overall system complexity while maintaining format specificity when needed.
Data Source
AI summary
Systems and methods are provided for a machine learning (ML) pipeline with a unified framework. A training data adapter receives training data in a training data format, processes the training data to match a pipeline data format of a machine learning pipeline, and transmits reformatted training data to the machine learning pipeline. The machine learning pipeline receives and processes the reformatted training data to train a machine learning model in the machine learning pipeline in a development environment, and further executes the machine learning model in a production environment. An artifact adapter receives training artifacts that were produced while training the machine learning model, and processes the training artifacts to update the machine learning model in the machine learning pipeline.