Automated MLOps Framework for Multi-Tenant Cloud Model Refresh
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Solution Overview
Problem
In multi-tenant cloud computing environments, machine learning (ML) models used for forecasting cloud usage become inaccurate and obsolete over time, leading to inefficiencies in storage, processing, and cost optimization, as existing systems require manual intervention by data scientists for model updates and lack automated mechanisms for identifying and refreshing models based on tenant-specific needs.
Innovation Solution
A dynamic Model Ops (MLOps) framework is implemented, utilizing a dynamic model selector (DMS) and dynamic configuration for model monitoring (DCMM) to automatically select and refresh ML models based on tenant configuration profiles, accuracy thresholds, and operational constraints, ensuring models remain accurate and up-to-date with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention by data scientists is used for model updates, then model accuracy can be maintained through expert judgment, but system complexity increases and productivity decreases
Solution Approach 1:
The system implements automated model monitoring and refresh capabilities that operate without manual intervention. The multi-tenant cloud server automatically monitors model performance metrics, compares them against predefined thresholds, and triggers model retraining or replacement when degradation is detected, enabling the system to self-maintain model accuracy while eliminating the need for continuous manual oversight
Solution Approach 2:
The system establishes a closed-loop feedback mechanism where model performance is continuously monitored, evaluated against accuracy thresholds, and used to automatically trigger refresh operations. This feedback-driven approach ensures model accuracy is maintained through objective performance metrics rather than subjective manual assessment, improving both reliability and productivity
2Reliability
If frequent model monitoring is implemented, then model accuracy can be maintained through timely detection of degradation, but computing resources and time are consumed
Solution Approach 1:
The system dynamically adjusts monitoring frequency and resource allocation based on model performance parameters and tenant-specific configurations. When models are performing well, monitoring intensity is reduced; when degradation is detected or models are critical to business operations, monitoring frequency increases. This parameter-based adaptation optimizes the balance between maintaining model accuracy and managing computing resource consumption
Solution Approach 2:
The system implements differentiated monitoring strategies where not all models are monitored at the same intensity. Critical models that directly impact tenant business operations receive more frequent and thorough monitoring, while less critical models are monitored at lower intensities. This partial action approach ensures adequate model accuracy maintenance while avoiding excessive resource consumption on non-critical models
3Productivity
If automated model selection and deployment is implemented, then productivity increases through reduced manual intervention, but system complexity increases
Solution Approach 1:
The system implements a universal automated model management platform that handles multiple functions including model selection, deployment, monitoring, and refresh operations through a single integrated system. This multi-functional approach increases productivity by automating the entire model lifecycle while managing complexity through consolidation rather than proliferation of separate systems
Solution Approach 2:
The system introduces an automated model management intermediary layer that sits between the model registry and tenant deployments. This intermediary handles the complexity of automated model selection, versioning, and deployment logic, shielding tenants from underlying system complexity while enabling high-productivity automated operations. The intermediary acts as a mediator that translates high-level tenant requirements into specific model deployment actions
4Adaptability or versatility
If tenant-specific model configurations are maintained, then adaptability to tenant needs is improved, but storage requirements increase
Solution Approach 1:
The system merges common model components and features across multiple tenants to reduce redundant storage. By identifying and consolidating shared model elements while maintaining tenant-specific customizations through configuration metadata rather than duplicate model copies, the system achieves high adaptability to tenant needs while minimizing overall storage requirements through intelligent resource sharing
Data Source
AI summary
A computer implemented system, method, and computer program product are disclosed for managing a machine learning model operation (MLOps) for cost forecasting models. The MLOps receives a request for a subscription machine learning (ML) model by a tenant having a corresponding tenant configuration profile and automatically selects a subscription ML model in a model registry based on the tenant configuration profile. The MLOps deploys the selected subscription ML model to the tenant and monitors usage by the tenant of the currently operating ML model at a pre-determined refresh frequency. The MLOps determines whether the currently operating ML model exceeds a pre-determined accuracy threshold, and automatically deploys a second subscription ML model from the model registry to the tenant in place of the currently operating ML model in response to the pre-determined accuracy threshold being exceeded.