Multi-model Inference Pipeline Architecture for Cloud Resource Optimization
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Solution Overview
Problem
Current cloud computing systems are resource-intensive and inefficient when handling multiple inference pipelines, leading to performance degradation and redundancy, as they are designed for single models and inputs, making them unsuitable for scalable multi-model and multi-input scenarios.
Innovation Solution
A multi-model train and inference pipeline architecture that combines multiple machine learning models into a single container, allowing for joint nested inferences across different data types, reducing resource usage and improving computational efficiency by utilizing a single inference model for predicting multiple outputs from various subcategories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple inference pipelines are run concurrently to handle multiple models and inputs, then the system can provide multi-model inferences, but computational resources (CPU, memory, GPU) are significantly consumed leading to resource contention and performance degradation
Solution Approach 1:
The patent combines multiple inference pipelines into a single unified pipeline that can process multiple models and inputs concurrently. The system merges multiple data preparation containers, model training containers, and inference containers into an integrated architecture where a single inference container orchestrates multiple models, reducing overall resource consumption while maintaining multi-model capability
Solution Approach 2:
The inference container is designed as a universal component that can handle multiple data types and model types simultaneously. The system creates a multi-functional inference pipeline that processes tabular data, time-series data, and unstructured data through a single container, eliminating the need for separate dedicated containers for each model type
2Reliability
If separate inference pipelines are used for each model to ensure proper resource allocation, then resource management becomes manageable, but the system complexity increases and redundancy is introduced
Solution Approach 1:
The patent merges multiple separate inference pipelines into a single unified pipeline architecture. Instead of maintaining separate containers for each model, the system combines them into one inference container that manages multiple models internally, reducing architectural complexity while preserving resource allocation control through centralized management
Solution Approach 2:
The system implements a nested structure where multiple model containers are nested within a single inference container. This hierarchical organization allows the outer container to manage resources for all inner models while the inner containers maintain their specific model logic, reducing overall system complexity through structured nesting
3Use of energy by moving object
If a single pipeline model is used to reduce resource consumption, then computational efficiency improves, but the system cannot scale to multiple models and multiple inputs
Solution Approach 1:
The patent transforms a single pipeline model into a universal multi-functional pipeline that can handle multiple data types (tabular, time-series, unstructured) and multiple model types simultaneously. The single inference container is designed to process diverse inputs and generate multiple outputs, providing scalability without requiring multiple separate pipelines
Solution Approach 2:
The system implements dynamic capabilities in the single pipeline model, allowing it to adapt its processing logic based on the input data type and model requirements. The inference container dynamically selects and applies appropriate processing methods for different data types, enabling the single model to scale to handle multiple models and inputs efficiently
Data Source
AI summary
Various examples are directed to providing a multi-model training and inference pipeline and environment using machine learning for a cloud environment.