InfoQ Homepage Machine Learning Content on InfoQ
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AI, ML, and Data Engineering InfoQ Trends Report—August 2022
In this annual report, the InfoQ editors discuss the current state of AI, ML, and data engineering and what emerging trends you as a software engineer, architect, or data scientist should watch. We curate our discussions into a technology adoption curve with supporting commentary to help you understand how things are evolving.
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Building Neural Networks with TensorFlow.NET
TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. TensorFlow.NET is a library that provides a .NET Standard binding for TensorFlow. In this article, the author explains how to use Tensorflow.NET to build a neural network.
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What You Should Know before Deploying ML in Production
What should you know before deploying machine learning projects to production? There are four aspects of Machine Learning Operations, or MLOps, that everyone should be aware of first. These can help data scientists and engineers overcome limitations in the machine learning lifecycle and actually see them as opportunities.
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Using Machine Learning for Fast Test Feedback to Developers and Test Suite Optimization
Software testing, especially in large scale projects, is a time intensive process. Test suites may be computationally expensive, compete with each other for available hardware, or simply be so large as to cause considerable delay until their results are available. The article explores optimizing test execution, saving machine resources, and reducing feedback time to developers.
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InfoQ Mobile and IoT Trends Report 2022
This report summarizes the views of the InfoQ editorial team and of several practitioners from the software industry about emerging trends in a number of areas that we collectively label the mobile and IoT space. This is a rather heterogeneous space comprising devices and gadgets from smartphones to smart watches, from IoT appliances to smart glasses, voice-driven assistants, and so on.
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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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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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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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Add Augmented Reality Effects to Android Apps Using the VrFace Library
In this article, we describe how to create augmented reality applications for Android using the open-source VrFace library. In the process, you will also learn about basic vision and ML techniques, including how to process camera frames using OpenCV and how to detect faces and facial feature points using appropriate models.