InfoQ Homepage application performance management Content on InfoQ
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Instrumentation, Observability & Monitoring of Machine Learning Models
Josh Wills discusses the monitoring and visibility needs of machine learning models in order to bridge gaps between ML practitioners and DevOps.
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Expect the Unexpected: How to Deal with Errors in Large Web Applications
Mats Bryntse demonstrates how to implement error monitoring in a web app and also shows how to reproduce errors without having to ask the user for a step-by-step description.
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Applying Concurrency Cookbook Recipes to SPEC JBB
Monica Beckwith talks about how she followed the recipes appearing in Doug Lea's cookbook and applied them to SPEC JBB, and reports her findings.
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Spinnaker and the Distributed Monorepo
Jon Schneider presents a continuous delivery platform with application monitoring, automated canary analysis, and organization-wide code search showing how to identify and repair applications.
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Enabling .NET Apps with Monitoring and Management Using Steeltoe
Dave Tillman discusses using the Steeltoe Management frameworks to enable a .NET application with performance monitoring, management diagnostic endpoints, and distributed tracing on PCF.
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Restoring Confidence in Microservices: Tracing That's More Than Traces
Ben Sigelman talks about rethinking distributed tracing in terms of the most vital organizational problems that microservices introduced.
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Jupyter Notebooks: Interactive Visualization Approaches
Chakri Cherukuri talks about how to understand and visualize machine learning models using interactive widgets and introduces the widget libraries.
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Yes, I Test in Production (And So Do You)
Charity Majors talks about testing in production and the tools and principles of canarying software and gaining confidence in a build, also instrumentation and observability .
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Radical Realizations with Tracing & Metric Visualizations
David Crawford, Sean Keery share insights about combining tracing data & metrics with animated traffic dashboards to convey a more comprehensive understanding of the variables in play.
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Monitoring AI with AI
Iskandar Sitdikov discusses a solution, tooling and architecture that allows an ML engineer to be involved in delivery phase and take ownership over deployment and monitoring of ML pipelines.
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Expect the Unexpected: How to Handle Errors Gracefully
Bastian Hoffman discusses monitoring and logging errors, showing how to handle them, covering deployment strategies with circuit breakers, and reducing functionality to minimize impact.
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Deep Learning for Application Performance Optimization
Zoran Sevarac presents his experience and best practice for autonomous, continuous application performance tuning using deep learning.