InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Federated Machine Learning and Edge Systems
At QCon Plus 2021, Katharine Jarmul spoke about machine learning on edge devices using federated machine learning. Some key takeaways were: federated machine learning is useful for edge devices with limited network bandwidth and can improve data privacy; and learning on edge devices can improve data diversity and allow for predictions even when the device is no longer connected.
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The Major Software Industry Trends from 2021 and What to Watch in 2022
In this podcast summary Thomas Betts, Wes Reisz, Shane Hastie, Charles Humble, Srini Penchikala, and Daniel Bryant discuss what they have seen in 2021 and speculate a little on what they hope to see in 2022. Topics explored included: hybrid working and the importance of ethics and sustainability within technology.
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The Next Evolution of the Database Sharding Architecture
In this article, author Juan Pan discusses the data sharding architecture patterns in a distributed database system. She explains how Apache ShardingSphere project solves the data sharding challenges. Also discussed are two practical examples of how to create a distributed database and an encrypted table with DistSQL.
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Getting Rid of Wastes and Impediments in Software Development Using Data Science
This article presents how to use data science to detect wastes and impediments, and concepts and related information that help teams to figure out the root cause of impediments they struggle to get rid of. The knowledge discovered during research includes an expanded waste classification, and the use of trends to uncover undesired situations like hidden delayed backlog items and defects trends.
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Developing Deep Learning Systems Using Institutional Incremental Learning
Institutional incremental learning promises to achieve collaborative learning. This form of learning can address data sharing and security issues, without bringing in the complexities of federated learning. This article talks about practical approaches which help in building an object detection system.
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Anomaly Detection Using ML.NET
In this article, the author introduces the concepts of Anomaly Detection using the Randomized PCA method. The theory behind the concepts is explained and exemplified. The method is demonstrated with a real-world scenario implemented using C# and ML.NET.
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Is Artificial Intelligence Taking over DevOps?
AI tools are slowly replacing the role of the developer – just as DevOps did before – and will eventually supplant DevOps entirely. Assessing whether that prediction is true is tricky. In this article, we’ll look at what AI promises for the development process, assess whether it can really ever take over from human developers, and what DevOps is likely to look like in a decades’ time.
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AI, ML and Data Engineering InfoQ Trends Report - August 2021
How AI, ML and Data Engineering are evolving in 2021 as seen by the InfoQ editorial team. Topics discussed include deep learning, edge deployment of machine learning algorithms, commercial robot platforms, GPU and CUDA programming, natural language processing and GPT-3, MLOps, and AutoML.
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Benefits of Loosely Coupled Deep Learning Serving
As deep networks are becoming more specialized and resource-hungry, serving such networks on acceleration hardware in tight-budget environments is also becoming difficult. Instead of using API frameworks, loosely coupled components can be preferred as an alternative. They bring high controllability, easy adaptability, transparent observability, and cost-effectiveness when serving deep networks.
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Accelerating Deep Learning on the JVM with Apache Spark and NVIDIA GPUs
In this article, authors discuss how to use the combination of Deep Java Learning (DJL), Apache Spark v3, and NVIDIA GPU computing to simplify deep learning pipelines while improving performance and reducing costs. They also show the performance comparison of this solution with GPU vs CPU hardware, using Amazon EMR and NVIDIA RAPIDS Accelerator.
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Deep Diving into EF Core: Q&A with Jeremy Likness
Entity Framework (EF) Core is a cross-platform, extensible, open-source object-database mapper for .NET. Since its first release in 2016, EF Core evolved until reaching its current form: a powerful and lightweight .NET ORM. InfoQ interviewed Jeremy Likness, program manager for .NET Data at Microsoft, to understand more about EF Core and what we should expect for its next release later this year.
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Why a Serverless Data API Might Be Your Next Database
In this article, author Pieter Humphrey discussed database as a service (DBaaS) and serverless data API for cloud based data management.