In this session, the Seldon team unveils the new Model Performance Metrics (MPM) module, designed to help teams monitor classification and regression models in production using standard performance metrics like accuracy, precision, recall, and more. Whether you’re deploying models with Seldon Core 2 or using our broader data science pack, this webinar showcases how to track quality, guide retraining, and build trust in your ML systems.
About
Learnings
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Why Monitoring Matters: Silent model degradation increases risk; real-time metrics enable early detection and response.
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How It Works: Capture model predictions and feedback, store them, and calculate metrics on demand.
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Metrics Supported: Includes accuracy, precision, recall, F1, RMSE, MAE, MSE, and confusion matrix.
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Flexible Visualisation: Plot metrics over time in Jupyter or Grafana dashboards for continuous insights.
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Demo Walkthrough: Live demo shows setup, feedback logging, bucket-based metric analysis, and dashboarding.
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End-to-End Value: Improve reliability, trust, and auditability while reducing unnecessary retraining.
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Part of the Data Science Pack: Complements Alibi Detect and Alibi Explain for full lifecycle model monitoring.
Matthew Lowdon
Matthew Lowdon is the Principal Product Manager at Seldon, leading strategy and innovation for AI and ML deployment solutions. With over a decade of experience spanning product management, quantitative analysis, and machine learning applications, Matthew has driven impactful product strategies in industries from finance to advanced AI platforms. Holding a Physics MA from the University of Oxford, Matthew blends deep technical understanding with a proven track record in delivering products that meet strategic business goals.
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