InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Analytics Zoo: Unified Analytics + AI Platform for Distributed Tensorflow, and BigDL on Apache Spark
In this article we described how Analytics Zoo can help real-world users to build end-to-end deep learning pipelines for big data, including unified pipelines for distributed TensorFlow and Keras on Apache Spark, easy-to-use abstractions such as transfer learning and Spark ML pipeline support, built-in deep learning models and reference use cases, etc.
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Back to the Future with Relational NoSQL
This article outlines some of the consistency issues NoSQL databases have with distributed transactions, showing how FaunaDB has solved the problems using the Calvin protocol and a virtual clock.
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Sentiment Analysis: What's with the Tone?
Sentiment analysis is widely applied in voice of the customer (VOC) applications. In this article, the authors discuss NLP-based Sentiment Analysis based on machine learning (ML) and lexicon-based approaches using KNIME data analysis tools.
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Spark Application Performance Monitoring Using Uber JVM Profiler, InfluxDB and Grafana
In this article, author Amit Baghel discusses how to monitor the performance of Apache Spark based applications using technologies like Uber JVM Profiler, InfluxDB database and Grafana data visualization tool.
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Seth James Nielson on Blockchain Technology for Data Governance
Seth James Nielson recently hosted a tutorial workshop at Data Architecture Summit 2018 Conference about Blockchain technology and its impact on data architecture and data governance.
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Apache Kafka: Ten Best Practices to Optimize Your Deployment
Author Ben Bromhead discusses the latest Kafka best practices for developers to manage the data streaming platform more effectively. Best practices include log configuration, proper hardware usage, Zookeeper configuration, replication factor, and partition count.
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Natural Language Processing with Java - Second Edition: Book Review and Interview
Natural Language Processing with Java - Second Edition book covers the Natural Language Processing (NLP) topic and various tools developers can use in their applications. Technologies discussed in the book include Apache OpenNLP and Stanford NLP. InfoQ spoke with co-author Richard Reese about the book and how NLP can be used in enterprise applications.
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The 2018 InfoQ Editors’ Recommended Reading List: Part One
As part of our core values of sharing knowledge, the InfoQ editor team has listed and commented on their most recent recommended reading.
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Democratizing Stream Processing with Apache Kafka® and KSQL - Part 2
In this article, author Robin Moffatt shows how to use Apache Kafka and KSQL to build data integration and processing applications with the help of an e-commerce sample application. Three use cases discussed: customer operations, operational dashboard, and ad-hoc analytics.
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How to Choose a Stream Processor for Your App
Stream Processors are software platforms that enable users to respond to incoming data streams faster compared to traditional batch data processing. In this article, the authors discuss the stream processor reference architecture, key features required by most streaming applications and optional features that can be selected based on specific use cases.
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Analyzing and Preventing Unconscious Bias in Machine Learning
This article is based on Rachel Thomas’s keynote presentation, “Analyzing & Preventing Unconscious Bias in Machine Learning” at QCon.ai 2018. Thomas talks about the pitfalls and risk the bias in machine learning brings to the decision-making process. She discusses three use cases of machine learning bias.
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Key Takeaway Points and Lessons Learned from QCon New York 2018
This year, at the seventh annual QCon New York, we had in total 143 speakers across the 117 sessions, workshops, AMAs, Open Spaces and mini-workshops. Topics included containers and orchestration, machine learning, ethics, modern user interfaces, microservices, blockchain, empowered teams, modern Java, DevEX, Serverless, chaos and resilience, Go, Rust, Elixir, and security.