Dynamic ML Model Deployment via Accuracy-Based Traffic Routing
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Solution Overview
Problem
Implementing machine learning models in practice is challenging due to algorithmic and mathematical complexities, requiring significant time and resources, and existing methods struggle with dynamic changes in environments and use cases, leading to inefficient model deployment and accuracy evaluation.
Innovation Solution
A dynamic accuracy-based experimentation and deployment system that evaluates machine learning models in real-time, using primary and secondary models to assess performance, redirect traffic based on model performance, and implement ensemble techniques for improved inference accuracy, allowing for seamless transition between models and optimizing model usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are deployed using traditional methods, then model generation can be completed, but the deployment process is extremely time and resource consumptive and filled with complexities
Solution Approach 1:
The system performs preliminary actions by conducting accuracy evaluations and shadow running of models before full deployment. Multiple models are pre-evaluated and ranked based on their performance metrics, allowing the system to have ready-to-deploy models when traffic demands change, thus reducing deployment time and resource consumption.
Solution Approach 2:
The system implements dynamic model deployment by continuously monitoring model accuracy and traffic patterns. The model selector dynamically adjusts which models receive traffic based on real-time performance evaluation, allowing the system to adapt to changing conditions without requiring complete redeployment of models, thereby improving deployment efficiency and reducing time loss.
2Adaptability or versatility
If traditional model deployment methods are used, then models can be deployed, but they struggle with dynamic changes in environments and use cases, requiring complete regeneration of new models
Solution Approach 1:
The system prepares multiple candidate models in advance with different specializations and training data sets. When environmental conditions or use cases change, the system can immediately switch to a pre-prepared model that is suited for the new conditions, avoiding the need for complete model regeneration and improving adaptability while maintaining productivity.
Solution Approach 2:
The system changes parameters by adjusting which models are active and how traffic is distributed among them based on changing environmental conditions. The model selector modifies traffic distribution parameters in response to accuracy evaluations, allowing the system to adapt to new conditions by parameter adjustment rather than complete model regeneration.
3Measurement precision
If multiple models are evaluated and traffic is redirected based on performance, then inference accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces a model selector as an intermediary component that manages the complexity of evaluating and routing traffic among multiple models. This intermediary abstracts the complex accuracy evaluation and model comparison processes, presenting a simplified interface that maintains high inference accuracy while reducing the apparent system complexity to users and operators.
Solution Approach 2:
The system implements feedback mechanisms where model accuracy is continuously evaluated and this information feeds back to the model selector, which adjusts traffic distribution accordingly. This automated feedback loop maintains high inference accuracy by directing traffic to the best-performing models while managing system complexity through automated decision-making rather than manual intervention.
Data Source
AI summary
Techniques for dynamic accuracy-based experimentation and deployment of machine learning (ML) models are described. Inference traffic flowing to ML models and the accuracy of the models is analyzed and used to ensure that better performing models are executed more often via model selection. A predictive component can evaluate which model is more likely to be accurate for certain input data elements. Ensemble techniques can combine inference results of multiple ML models to aim to achieve a better overall result than any individual model could on its own.