InfoQ Homepage Performance Content on InfoQ
-
Getting Data Science to Production
Sarah Aerni covers the nuts and bolts of the Einstein Platform, a system that enables the automation and scaling of Artificial Intelligence to 1000s of customers, each with multiple models.
/presentations/einstein-platform-ai/en/mediumimage/Sarbig-1515888330792.JPG)
-
ML for Question and Answer Understanding @Quora
Nikhil Dandekar discusses how Quora extracts intelligence from questions using machine learning, including question-topic labeling, removing duplicate questions, ranking questions & answers, and more.
/presentations/quora-ml/en/mediumimage/Nikbig-1515814953724.JPG)
-
Go Programming Language
Dave Cheney discusses the Go language: writing and interpreting benchmarks, using performance tools built into the Go runtime, GC and writing GC-friendly code.
/presentations/go-programming-language/en/mediumimage/Davbig-1515034755120.JPG)
-
Scaling Slack
Bing Wei examines the limitations that Slack's back-end ran into and how they overcame them to scale from supporting small teams to serving large organizations of hundreds and thousands of users.
/presentations/slack-scalability/en/mediumimage/ScalingSlack-1517499445761.jpg)
-
Monitoring Modern Architectures with Data Science
Dave Casper talks about how modern data science and algorithms are being applied to "fight machines with machines".
/presentations/modern-architectures-data-science/en/mediumimage/Davbig-1514519899503.JPG)
-
Handling Billions of Edges in a Graph Database
Michael Hackstein discusses graph databases, the current scalability problems and their solutions.
/presentations/graph-database-scalability/en/mediumimage/Micbig-1513911472792.JPG)
-
Performance Mythbusting Panel
The panelists answer audience questions on real-world applied performance proofs across stacks including Java, .NET and Python.
/presentations/performance-mythbusting-panel/en/mediumimage/Panelbig-1513738176394.jpg)
-
Control Flow Integrity Using Hardware Counters
J. Butler and C. Pierce present a system for early detection and prevention of unknown exploits. Their system uses Performance Monitoring Unit hardware to enforce coarse-grained Control Flow Integrity
/presentations/control-flow-integrity-counters/en/mediumimage/Doibig-1513384172140.JPG)
-
Scale @Reddit Triple Team Size w/o Losing Control
Nick Caldwell discusses his engineering team's approach to Agile development as they scaled from 40 to 120 engineers.
/presentations/scale-reddit/en/mediumimage/ScaleRedditTripleTeamSizeWOLosingControl-1517494498454.jpg)
-
The Anatomy of a Distributed System
Tyler McMullen talks through the components and design of a real system, built to perform very high volumes of health checks, done across a cluster of machines for reliability and scalability.
/presentations/health-distributed-system/en/mediumimage/TheAnatomyOfADistributedSystem-1517493913337.jpg)
-
Three Baseline Metrics
Mike Burns outlines three metrics -cycle time, throughput, and work item size- a team can use to help improve team performance, and allow for the right decisions to be made at the right time.
/presentations/3-metrics/en/mediumimage/Mikbig-1512350441357.JPG)
-
Code Archaeology
David Mitchell shows how to create visualizations out of code: building a map for a large, legacy code base, creating visuals without drawing, and explaining a roadmap to bring code under control.
/presentations/code-visualization/en/mediumimage/Davbig-1511918609472.JPG)