Staging Environment for Machine Learning Workflow Testing
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Solution Overview
Problem
Conventional techniques fail to effectively measure the impact of machine learning models and feature sets on production systems, leading to potential system performance issues and user experience problems due to late detection of latency and errors, which can negatively impact scalability and uptime.
Innovation Solution
A framework for staging tests that deploys machine learning workflows onto a staging environment, directing live production traffic to simulate real-world conditions without affecting the production environment, allowing for the collection of metrics such as latency and error rates to assess system impact and correctness before deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are deployed directly to production environment, then system impact can be measured in real-world conditions, but latency and errors are detected late causing negative impact on system performance and user experience
Solution Approach 1:
The patent implements a staging environment where machine learning models are tested and validated before being deployed to production. This preliminary testing phase allows latency and errors to be detected and addressed before they impact production systems, resolving the contradiction between measuring system impact in real-world conditions and detecting issues early.
Solution Approach 2:
The patent introduces a staging environment as an intermediary between development and production. This intermediate layer enables models to be tested with live traffic in a controlled setting, allowing performance measurement without directly exposing production systems to unvalidated models, thus preventing late detection of issues.
2Measurement precision
If machine learning models with large numbers of features and complex architectures are used, then model accuracy and insights are improved, but computational resources and processing time are significantly increased
Solution Approach 1:
The patent enables preliminary testing of machine learning models in a staging environment before production deployment. This allows computational resources to be allocated efficiently by testing complex models in advance, validating their performance and resource consumption patterns before full deployment, thereby avoiding wasteful allocation of resources to unvalidated complex models.
3Adaptability or versatility
If new features or feature producers are added to the system, then model capabilities are enhanced, but errors in execution and output can negatively impact downstream services and user experiences
Solution Approach 1:
The patent implements a staging environment where new features and feature producers are tested and validated before being integrated into production models. This preliminary validation ensures that new features do not introduce errors in execution or output that would negatively impact downstream services, while still allowing the system to adapt to enhanced capabilities.
Data Source
AI summary
The disclosed embodiments provide a system for testing machine learning workflows. During operation, the system obtains a configuration for a staging test of a machine learning model, wherein the configuration includes a model name for the machine learning model, a duration of the staging test, and a use case associated with the machine learning model. Next, the system selects a staging test host for the staging test. The system then deploys the staging test on the staging test host in a staging environment, wherein the deployed staging test executes the machine learning model based on live traffic received from a production environment. After the staging test has completed, the system outputs a set of metrics representing a system impact of the machine learning model on the staging test host.