InfoQ Homepage Presentations
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Cybercrime and the Developer: How to Start Defending against the Darker Side
Steve Poole discusses actions one can take (and some behaviors one must change) to create a more secure Java application for the cloud.
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Being Human and Professional Is Mutually Exclusive
Gitte Klitgaard discusses being human and having feelings at work.
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Julia: A Modern Language for Modern ML
Simon Byrne and Viral Shah talk about Julia, a modern high-performance, dynamic language for technical computing, with many features which make it ideal for machine learning.
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Latency Sensitive Microservices
Peter Lawrey discusses the overlap between microservices and a trading system, how to make microservices easy to test and performant, and how to easily maintain a trading system.
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Building a Scalable, Distributed Backend for Mobile Games
Petri Kero presents how Ministry of Games is tackling the scalability problem with distributed Elixir.
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JavaScript Futures: ES2017 and the Road ahead
Jeff Strauss discusses some of the new and proposed features of JavaScript, explaining the ES.Next maturity stages and the TC39 review process.
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Why Do Companies Build APIs?
Alex Wilson discusses the reasons why companies build APIS including the financial reasons and the desire for digital transformation.
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IoT and Microservices in the Home
Fred George explores the use of asynchronous microservices to implement a home IoT environment of heterogeneous devices, including lights and motion sensors on a J2ME-like environment.
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How the Adoption of IoT Will Shape the Future of Corporate Learning
Sanjay Parker explores how IoT will impact corporate education and learning beyond 2020, and how IoT can fuel knowledge and learning for organizations.
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Joy of Coding 2017–Lightning Talks
The speakers give seven talks of five minutes each, presenting points of view or words of wisdom that might be of interest to software developers.
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Fighting Online Fraud and Abuse with Large-Scale Machine Learning at Sift Science
Jacob Burnim discusses Sift’s approach to building a ML system to detect fraud and abuse, including training models, handling imbalanced classes, sharing learning, measuring performance, etc..
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Systems That Learn
Stephen Buckley discusses the Systems That Learn initiative which aims to create systems that learn by combining expertise in Systems and Machine Learning.
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