Digital Assistant Performance Controller for Model Drift Visibility
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Solution Overview
Problem
Conventional digital assistant platforms lack visibility into the performance of deployed models, failing to provide insights when the digital assistant performs poorly or degrades over time, and do not monitor data characteristics such as bias, drift, and outliers.
Innovation Solution
A performance controller with microservices for model explainability and data characteristic detection is integrated into the digital assistant platform, providing model interpretability and monitoring data bias, data drift, and outliers through explanation models and ensembling techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional digital assistant platforms are deployed without performance monitoring, then device complexity is reduced, but reliability deteriorates due to lack of visibility into model performance degradation
Solution Approach 1:
A performance controller is introduced as an intermediary component between the machine learning model and the digital assistant platform. This controller includes explanation models that generate explanations for model predictions and detect data characteristics such as bias, drift, and outliers, providing visibility into model performance without requiring fundamental changes to the core platform architecture.
Solution Approach 2:
The performance monitoring functionality is segmented into separate explanation models and data characteristic detection models that operate independently from the main digital assistant platform. This modular approach allows performance monitoring to be added without increasing the complexity of the core system, as the monitoring components can be deployed and managed separately.
2Measurement precision
If multiple explanation models are applied to generate comprehensive performance metrics, then measurement precision is improved, but device complexity increases due to ensembling requirements
Solution Approach 1:
Multiple explanation models are merged into a unified performance controller that processes model predictions together. The controller combines outputs from different explanation models (such as integrated gradients, anchors, and similarity models) to generate comprehensive performance metrics, achieving high measurement precision through ensembling while managing complexity through unified architecture.
3Reliability
If continuous performance monitoring is implemented, then reliability is improved, but use of energy increases due to ongoing model evaluation
Solution Approach 1:
Performance monitoring is implemented as a periodic process rather than continuous evaluation. The performance controller evaluates model performance at intervals, generating explanations and detecting data characteristics at scheduled times. This periodic approach maintains reliability by regularly monitoring model performance while reducing energy consumption compared to continuous evaluation.
Data Source
AI summary
A method may include training, based at least on a first data, a machine learning model to perform one or more natural language processing tasks. The trained machine learning model may be deployed to a production environment to support natural language based interactions with a digital assistant. A plurality of performance metrics may be generated to include explanation data associated with the deployed machine learning model operating on a second data as well as one or more data characteristics of the second data such as data drift, data bias, and outliers. A user interface may be generated to display, at a client device, a visual representation of at least a portion of the plurality of performance metrics associated with the deployed machine learning model. Related methods and computer program products are also disclosed.