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Testing and Monitoring Machine Learning Model Deployments

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Testing and Monitoring Machine Learning Model Deployments
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ML testing strategies, shadow deployments, production model monitoring and more.Hot & New
by Christopher Samiullah, Soledad Galli

What you'll learn


  • Machine Learning System Unit Testing
  • Machine Learning System Integration Testing
  • Machine Learning System Differential Testing
  • Shadow Deployments (also known as Dark/Decoy launches)
  • Statistical Techniques for Assessing Shadow Deployments
  • Monitoring ML System with Metrics (Prometheus & Grafana)
  • Monitoring ML Systems with Logs (Kibana & the Elastic Stack)
  • The Theory Around Continuous Delivery for Machine Learning

Learn how to test & monitor production machine learning models.

What is model testing?
You’ve taken your model from a Jupyter notebook and rewritten it in your production system. Are you sure there weren’t any mistakes when you moved from the research environment to the production system? How can you control the risk before your deployment? ML-specific unit, integration and differential tests can help you to minimize the risk.
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