InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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The InfoQ eMag: QCon 2018 Retrospective
We take a look back at best QCon highlights in 2018, including QCon London, QCon.AI, QCon New York and QCon San Francisco.
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The InfoQ eMag: Tech Ethics
In an ideal world, devs would like to be ethical in their work but they ultimately don’t consider it to be part of their responsibilities. This eMag sets out to understand why they might feel that way and whose job it is to take reasonable steps to ensure that tech products don’t harm users or anyone else.
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The Morning Paper Issue 8 - AI Edition
Welcome to this AI-themed edition of The Morning Paper Quarterly. We've selected five paper write-ups which first appeared on The Morning Paper blog over the last year.
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The InfoQ eMag: Real-World Machine Learning: Case Studies, Techniques and Risks
Machine learning (ML) and deep-learning technologies like Apache Spark, Flink, Microsoft CNTK, TensorFlow, and Caffe brought data analytics to the developer community. This eMag focuses on the current landscape of ML technologies and presents several associated real-world case studies.
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Big Data Processing with Apache Spark
In this mini-book, the reader will learn about the Apache Spark framework and will develop Spark programs for use cases in big-data analysis. The book covers all the libraries that are part of Spark ecosystem, which includes Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX.
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The InfoQ eMag: Streaming Architecture
This InfoQ emag aims to introduce you to core stream processing concepts like the log, the dataflow model, and implementing fault-tolerant streaming systems.
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The InfoQ eMag: Introduction to Machine Learning
InfoQ has curated a series of articles for this introduction to machine learning eMagazine, covering everything from the very basics of machine learning (what are typical classifiers and how do you measure their performance?) and production considerations (how do you deal with changing patterns in data after you’ve deployed your model?), to newer techniques in deep learning.
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The InfoQ eMag: Getting a Handle on Data Science
This eMag looks at data science from the ground up, across technology selection, assembling raw and unstructured data, statistical thinking, machine learning basics, and the ethics of applying these new weapons.
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The InfoQ eMag: The Current State of NoSQL Databases
This eMag focuses on the current state of NoSQL databases. It includes articles, a presentation and a virtual panel discussion covering a variety of topics ranging from highly distributed computations, time series databases to what it takes to transition to a NoSQL database solution.
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InfoQ eMag: Graph Databases
This eMag focuses on the graph database landscape and the real world use cases of graph databases. It includes articles and interviews covering topics like data modeling in graph databases and how companies use graph databases in their application. It also includes an article on full stack web development using a graph database.
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InfoQ eMag: Hadoop
Apache Hadoop is proving useful in deriving insights out of large amounts of data, and is seeing rapid improvements. Hadoop 2 now goes beyond Map-Reduce; it is more modular, pluggable and flexible and it fits a variety of use cases better. We explore this as well as some tools that can help utilize Hadoop better.
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InfoQ eMag: The Best of NoSQL
The InfoQ NoSQL eMag brings together a selection of popular NoSQL articles recently published on InfoQ.com. Get a complete overview of the current NoSQL movement, learn how NoSQL relates to the CAP Theorem, and get practical guidance on setting up and using a popular NoSQL database.
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An in-memory computing platform can add speed and scalability to your data-intensive applications
High Performance, Real-time Data Architecture for the Internet of Things
Learn how in-memory computing addresses the most common use cases and challenges associated with the real-time processing and storage of IoT-generated data.
In-Memory Computing Best Practices: Building a Foundation and Roadmap for Digital Transformation
In this eBook, you’ll learn best practices for establishing a sound and cost-effective in-memory computing foundation that focuses on the customer experience and more.
A Machine and Deep Learning Primer
Learn how in-memory computing can make machine and deep learning faster, while reducing complexity and expense.
Introducing the GridGain In-Memory Computing Platform White Paper
Improving Speed and Scalability with In-Memory Data Grids White Paper
451 Research Report: HOAP and In-Memory Computing: Accelerating Digital Business and Customer Experiences