InfoQ Homepage Presentations
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Ethics in Computing Panel
The panelists discuss the important points around privacy, security, safety online, and intent of software today.
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Data, GDPR & Privacy: Doing It "Right" without Losing It All
Amie Durr talks about the privacy concerns and subsequent regulations, and how to operate a business that does the "Right" thing for the consumer, without impact the ability to innovate and grow.
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Real-Time Functional Reactive Programming with Erlang
Peer Stritzinger, Kilian Holzinger discuss and show cyber-physical systems implemented with FRP-Erlang running on GRiSP, a bare-metal board running Erlang.
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Safely Creating Autonomy in the Workplace
Jasper Sonnevelt discusses how to create an environment for teams to work autonomously without worrying if they are working on the right things or not.
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Cryptoeconomics - The Application of Economic Systems, Incentives and Mechanisms
Jomari Peterson discusses crypto-economics and some of the significant considerations when designing a cryptocurrency.
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Agile in 2018
Martin Fowler reflects on Agile’s journey to become a mainstream methodology, along with some of the successes and failures encountered along the way.
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Break Up with Your Front-end Monolith
Elisabeth Engel discusses refactoring a front-end monolith, offering advice including building a parent app shell to deal with loading and routing child components, and avoiding certain obstacles.
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How to Use Data Responsibly
Emma Prest and Clare Kitching discuss practical, pragmatic and ethical data science, talking about real world experience from the work of DataKind UK.
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Privacy Ethics – A Big Data Problem
Raghu Gollamudi broadly covers best practices with respect to Data Management aspects from mapping Enterprise data to applying Data Protection rules like GDPR at petabyte scale.
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How Machines Help Humans Root Case Issues @ Netflix
Seth Katz discusses ways to build tools designed to enhance the cognitive ability of humans through automated analysis to speed root cause detection in distributed systems.
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Engineering Systems for Real-Time Predictions @DoorDash
Raghav Ramesh presents DoorDash’s thoughts on how to structure ML systems in production to enable robust and wide-scale deployment of ML, and shares best practices in designing engineering tooling.
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ML Data Pipelines for Real-Time Fraud Prevention @PayPal
Mikhail Kourjanski focuses on the architectural approach towards PayPal’s real-time service platform that leverages ML models, delivers performance and quality of decisions.
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