InfoQ Homepage Machine Learning Content on InfoQ
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Apache DolphinScheduler in MLOps: Create Machine Learning Workflows Quickly
In this article, author discusses data pipeline and workflow scheduler Apache DolphinScheduler and how ML tasks are performed by Apache DolphinScheduler using Jupyter and MLflow components.
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AutoML: the Promise vs. Reality According to Practitioners
Automation to improve machine learning projects comes from a noble goal, but true end-to-end automation is not available yet. As a collection of tools, AutoML capabilities have proven value but need to be vetted more thoroughly. Findings from a qualitative study of AutoML users suggest the future of automation for ML and AI rests in the ability for us to realize the potential of AutoMLOps.
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Streaming-First Infrastructure for Real-Time Machine Learning
This article covers the benefits of streaming-first infrastructure for two scenarios of real-time ML: online prediction, where a model can receive a request and make predictions as soon as the request arrives, and continual learning, when machine learning models are capable of continually adapting to change in data distributions in production.
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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.