InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Understanding Monads. A Guide for the Perplexed
With the current explosion of functional programming, the "monad" functional structure is once again striking fear into the hearts of newcomers. In this article, Introduction to Functional Programming course instructor Dr. Barry Burd clarifies this slippery critter.
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FPGAs Supercharge Computational Performance
Originally used in the development of new hardware, new, cloud-based FPGAs are making the technology more accessible. The dramatic improvements in speed and lower costs over traditional CPUs means more companies can start benefiting from the technology. FPGAs are fundamentally concurrent, which makes them an ideal tool for data-intensive, parallel processing problems.
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Building Reactive Systems Using Akka’s Actor Model and Domain-Driven Design
With the explosion of mobile and data-driven applications, users are demanding real-time access to everything everywhere. System resilience and responsiveness are essential business requirements. Businesses increasingly need to trade up to more flexible, "reactive" systems. To support reactive development, actor models with domain-driven design can fulfill modern resiliency requirements.
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Big Data and Big Money: The Role of Data in the Financial Sector
When we consider the 3Vs of big data— volume, velocity, and variety—it is hard to think of many sectors whose requirements fit so nicely into the guidelines at finance.
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User Anonymity on Twitter
This article explores how it is possible to measure how many Twitter users adopted anonymous pseudonyms, the correlation between content sensitivity and user anonymity, and whether it would be possible to build automated classifiers that would detect sensitive Twitter accounts.
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The Problem with AI
AI depends on "data janitorial" work, as opposed to science work, and there is a gulf between prototype and sandbox, and innovation and production.
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How Much Should We Trust Artificial Intelligence
Considerable buzz surrounds artificial intelligence, and, indeed, AI is all around us. As with any software-based technology, it is also prone to vulnerabilities. Here, the author examines how we determine whether AI is sufficiently reliable to do its job and how much we should trust its outcomes.
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Video Stream Analytics Using OpenCV, Kafka and Spark Technologies
What is the role of video streaming data analytics in data science space. Learn how to implement a motion detection use case using a sample application based on OpenCV, Kafka and Spark Technologies.
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Perspective on Architectural Fitness of Microservices
In this article we peel the onion of potential architectural fitness of microservices in the context of Master Data Management, and the challenges a microservices-based architecture may face when solving problem domains that require compute-intensive tasks, such as the calculation of expected losses on a portfolio of unsecured consumer credit.
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Key Takeaway Points and Lessons Learned from QCon New York 2017
The sixth annual QCon New York was the biggest yet, bringing together over 1,100 team leads, architects, project managers, and engineering directors - up from last year's record of 940. It was also the first to take place in our new home in Times Square.
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Apache Beam Interview with Frances Perry
InfoQ Interviews Apache Beam's Frances Perry about the impetus for using Beam and the future of the top-level open source project and covers the thoughts behind the programming model as well as some of the touch-points in integration with other data engineering tools like Apache Spark and Flink.
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Introducing FaunaDB Serverless Cloud
FaunaDB Serverless Cloud is the managed version of FaunaDB, a serverless, object-relational, globally replicated, strongly consistent, temporal database, that can be deployed on multiple clouds, such as AWS, GCP, and Azure, or on premises.