InfoQ Homepage Programming Content on InfoQ
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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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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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Continuous Deployment Made Easy with Skipper
Mark Pollack discusses building continuous delivery pipelines using existing CI products and application repositories by adding Skipper.
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Reactive Front-Ends with RxJS and Angular
Sergi Almar introduces the fundamentals of RxJS, explaining how to manage data streams like UI events, async HTTP requests, and WebSockets / SSE in a uniform way.
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Migrating to Angular 4 for Spring Developers
Gunnar Hillert discusses the challenges, experiences and reasons for migrating the Spring Cloud Data Flow Dashboard from using AngularJS 1.x to Angular 4.
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Simplifying Apache Geode with Spring Data
John Blum shows how to use the annotation-based configuration model to build an Apache Geode client-server application.
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Spring Driven Industrial IoT Utilizing Edge, Fog, and Cloud Computing
Mark Weislow and Barry Wood demo an end to end feedback workflow of Industrial Spring IoT.
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Fault Tolerance Is a Requirement, Not a Feature
Adar Danait and Lilian Ernest discuss best practices and recommendations for using Hystrix circuit breaker for microservices.
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Fuelling the AI Revolution with Gaming
Alison Lowndes talks about the HW & SW that comprise NVIDIA's GPU computing platform for AI, across PC to data center, cloud to edge, training to inference.
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AI Panel
Panelists attempt to demystify AI and answer questions from the public.
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Real-Time Data Analysis and ML for Fraud Prevention
Mikhail Kourjanski addresses the architectural approach towards the PayPal internally built real-time service platform, which delivers performance and quality of decisions.
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Deep Learning for Science
Prabhat discusses machine learning's impact on climatology, astronomy, cosmology, neuroscience, genomics, and high-energy physics, and the future of AI in powering scientific discoveries.
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