 Data Engineering and Machine Learning Special Report |
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| What's New in Azure Machine Learning? (news, May 09, 2018) | | PyTorch 1.0 Announced for Research and Production AI Projects (news, May 14, 2018) | | Developing Data and ML Pipelines at Stitch Fix (presentations, May 15, 2018) | | Cloud-Native Data: What Is it? Will it Solve the Data-DevOps Divide? (presentations, May 12, 2018) | | Machine Learning Pipeline for Real-Time Forecasting @Uber Marketplace (presentations, May 10, 2018) |
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| Top Viewed Content on InfoQ |
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| Tensorflow with Javascript Brings Deep Learning to the Browser (news, Apr 18, 2018) | | The InfoQ eMag: Streaming Architecture (books, Jan 07, 2018) | | Migrating Batch ETL to Stream Processing: A Netflix Case Study with Kafka and Flink (articles, Feb 08, 2018) | | Data Consistency in Microservice Using Sagas (presentations, Jan 31, 2018) | | Modern Big Data Pipelines over Kubernetes (news, Jan 08, 2018) |
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| The IEEE publishes an annual list of the Top 10 Technology Trends for each upcoming year. Making the list for 2018 are multiple topics surrounding artificial intelligence and machine learning. Deep learning comes in as the IEEE hottest trend for 2018. | | Propel is a new JavaScript scientific computing library leveraging GPU hardware for computations to support machine learning and other scientific computing in JavaScript. | | Since MongoDB acquired WiredTiger and their relational database storage engine, technologists have been speculating on when MongoDB would support multi-document transactions. With this week’s announcement, the expectation is that they’ll be ready this summer as part of MongoDB 4.0 |
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| Recent releases from Facebook and Google implement the most current deep-learning algorithms to take a crack at the challenging problem of machine object detection. |
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| Release 5.1 of Anaconda, the data science and machine learning platform, now includes Visual Studio Code as an IDE. This is part of a wider collaborative effort between Anaconda Inc. and Microsoft. |
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| The Netflix cloud database engineering team provides several flavors of data persistence as a service to microservice development teams. |
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| Are you planning to use artificial intelligence to solve enterprise problems? There is growing confirmation that you will need to address hindsight bias (aka label leakage) issue with your input data. |
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| Released this past summer, JPA 2.2 delivered some frequently requested enhancements: better alignment with Java 8 features like the Date and Time API and retrieval of a query result as a Stream. |
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| At QCon SF 2016, Frances Perry and Tyler Akidau presented “Fundamentals of Stream Processing with Apache Beam”, and discussed Google's Dataflow model and associated implementation of Apache Beam. |
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| Allen Wang talks about how Netflix addresses the issues of stability and scalability in a cloud environment by having many smaller and mostly immutable Kafka clusters with limited state changes. |
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| Rob Harrop describes both his own journey from traditional Software Engineer to AI/ML Engineer, and his experience building a development team with ML at the heart. |
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| Davis Shepherd and Eugen Cepoi discuss the evolution of ML automation at Netflix and how that lead them to build Meson, challenges faced and lessons learned automating thousands of ML pipelines. |
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| Marius Bogoevici introduces the Kafka Streams API and the Kafka Streams processing engine, showing how to write and deploy Kafka Streams applications using Spring Cloud Stream. |
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| Matthew Renze discusses what data science is, why it’s important, and how to prepare for it. He covers IoT, Big Data, ML, and how they are converging to create fully-autonomous intelligent systems. |
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