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Java at Speed
Gil Tene talks about getting the most of Java applications and understanding some of the optimizations the latest crop of JVMs are able to apply when running on the latest servers.
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Consensus: Why Can't We All Just Agree?
Heidi Howard takes a journey though the history of consensus, and looks ahead to the future of distributed consensus.
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Fast, Flexible and Functional Programming with OCaml
Gemma Gordon and Anil Madhavapeddy give a brief history of OCaml, and explain how they are unlocking its potential in the “new” world of browsers and IoT.
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C++ for Real-Time Communications in the Cloud
Thiya Ramalingam talks about what Zoom’s platform engineers have learned over the years from running a complete C++ stack in their back-end service.
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From Rocks to Rust: Our C to Rust Paradigm Shift
Esther Momcilovic talks about the reasons why Metaswitch chose Rust, and what it’s been like for the development teams getting to grips with this language.
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Rust 2018: An Epochal Release!
Steve Klabnik talks about where Rust is now, what new features are coming down the pipeline, how it's all being managed, and how this affects Rust's development in the future.
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Counting is Hard: Probabilistic Algorithms for View Counting at Reddit
Krishnan Chandra explains the challenges of building a view counting system at scale, and how Reddit used probabilistic counting algorithms to make scaling easier.
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Developing Data and ML Pipelines at Stitch Fix
Jeff Magnusson discusses thoughts and guidelines on how Stitch Fix develops, schedules, and maintains their data and ML pipelines.
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Building a Reliable Cloud-Based Bank in Java
Jason Maude talks about the server-side implementation of Starling Bank and shows how, even though Java is over two decades old, it can still be used for cutting edge applications.
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Attack Trees, Security Modeling for Agile Teams
Michael Brunton-Spall talks about Attack Trees, a new way of understanding how a system might be attacked and how to prioritize security measures to be implemented.
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Counterfactual Evaluation of Machine Learning Models
Michael Manapat discusses how Stripe evaluates and trains their machine learning models to fight fraud.
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Machine Learning Pipeline for Real-Time Forecasting @Uber Marketplace
Chong Sun and Danny Yuan discuss how Uber is using ML to improve their forecasting models, the architecture of their ML platform, and lessons learned running it in production.
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