InfoQ Homepage QCon Software Development Conference Content on InfoQ
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It's People, Stupid (People Are Stupid?)
Andy Walker’s proposition is that the reason things fail is usually people, not technology. This provocative discussion includes themes on ‘the broken human machine’, the ‘authority delusion’ and more
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Building Great Engineering Cultures Panel
The panelists discuss topics relating to the challenges of engineering culture development.
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Gimel: PayPal’s Analytics Data Platform
Deepak Chandramouli introduces and demos Gimel, a unified analytics data platform which provides access to any storage through a single unified data API and SQL.
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Understanding Software System Behavior with ML and Time Series Data
David Andrzejewski discusses how time series datasets can be combined with ML techniques in order to aid in the understanding of system behaviors in order to improve performance and uptime.
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Analyzing & Preventing Unconscious Bias in Machine Learning
Rachel Thomas keynotes on three case studies, attempting to diagnose bias, identify some sources, and discusses what it takes to avoid it.
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Building a Culture of Continuous Improvement
Kevin Goldsmith shows what Avvo has done to build a foundation for a continuous improvement culture.
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Observable JS Apps
Emily Nakashima talks about an event-driven approach to client-side observability for the most complicated parts of Honeycomb's customer-facing React app: the query builder.
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WebAssembly (And the Death of JavaScript?)
Colin Eberhardt looks at what's wrong with the way people are using JavaScript today and why they need WebAssembly.
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Reinventing npmjs.com
Katie Fenn talks about the process of architecting the new npmjs.com website, and examines how the changing landscape of development tooling has shaped it throughout its lifetime.
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Models in Minutes not Months: AI as Microservices
Sarah Aerni talks about how Salesforce built an AI platform that scales to thousands of customers.
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Understanding ML/DL Models using Interactive Visualization Techniques
Chakri Cherukuri discusses how to use visualization techniques to better understand machine learning and deep learning models.
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Interpretable Machine Learning Products
Mike Lee Williams discusses how interpretability can make deep neural networks models easier to understand, and describes LIME, an OS tool that can be used to explore what ML classifiers are doing.
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