Dynamic Resource Prediction for Cloud Native ML Workspaces
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Solution Overview
Problem
Existing approaches for deploying machine learning (ML) models in production environments face challenges such as static resource allocation, manual intervention for scaling, and lack of process automation for optimizing resource utilization, leading to inefficiencies and increased maintenance costs.
Innovation Solution
The development of a system that uses a Deep Neural Network (DNN) alongside reinforcement learning techniques to predict resource requirements for ML workspaces, allowing for dynamic and intelligent resource provisioning, thereby avoiding disruptions and optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static resource allocation is used for ML workspaces, then device complexity is reduced and ease of operation is improved, but resource utilization efficiency deteriorates and productivity decreases
Solution Approach 1:
The patent implements dynamic resource allocation by using reinforcement learning agents that continuously monitor workspace performance metrics and automatically adjust resource provisioning in real-time, transforming the static resource allocation system into a dynamic one that adapts to changing workload demands
Solution Approach 2:
The system enables self-service through automated reinforcement learning agents that independently make resource allocation decisions based on observed performance metrics, eliminating the need for manual intervention and achieving optimal resource utilization autonomously
2Productivity
If manual intervention is used for scaling ML workspaces, then ease of operation is maintained, but productivity decreases and loss of time increases
Solution Approach 1:
The reinforcement learning agents autonomously monitor workspace performance metrics and automatically trigger scaling operations when thresholds are met, enabling the system to self-manage resource provisioning without human intervention and significantly reducing the time required for scaling operations
Solution Approach 2:
The system implements continuous feedback loops where performance metrics from ML workspaces are monitored in real-time, fed back to the reinforcement learning agents, which then adjust resource allocation decisions accordingly, creating a closed-loop control system that responds dynamically to workload changes
3Adaptability or versatility
If cloud native architecture is used for ML platforms, then adaptability and versatility are improved, but reliability deteriorates due to fragmentation and maintenance complexity
Solution Approach 1:
The patent implements a universal orchestration layer that manages multiple cloud-native ML workspaces through a single unified interface, allowing the system to handle diverse workload types and infrastructure configurations while maintaining consistent reliability standards across all deployments
Solution Approach 2:
The system employs continuous monitoring and feedback mechanisms that track performance metrics across the cloud-native architecture, enabling real-time detection and correction of reliability issues while maintaining the adaptability benefits of cloud-native deployment
Data Source
AI summary
One example method includes receiving, by a workspace size predicting engine, a workspace provisioning request including resource requirement information that specifies one or more features that are to be included when a workspace is provisioned. The one or more features include at least a machine learning (ML) model that is to be run in the workspace. The method also includes predicting, by the workspace size predicting engine, the one or more resources for provisioning the workspace that corresponds to the workspace provisioning request.