InfoQ Homepage Machine Learning Content on InfoQ
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Generative AI: Shaping a New Future for Fraud Prevention
This article explores how generative AI affects fraud detection by reducing false positives and dynamically adapting to changing fraud patterns. This combination offers a potent preventive solution when integrated with machine learning. The efficacy and scalability of fraud prevention initiatives are enhanced by this innovative approach.
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InfoQ AI, ML, and Data Engineering Trends Report - September 2023
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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Reducing Verification Lead Time by 50% by Lowering Defect Slippage and Applying AI/ML Techniques
Can we increase our flexibility? Can we increase our test coverage? Can we increase our efficiency? And is it possible to reduce our verification lead-time by 50%? One company challenged itself with these questions. This article explores two important “‘pillars”’ of their testing strategy: shifting left and using state-of-the-art techniques to support verification activities.
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Minimising the Impact of Machine Learning on our Climate
This article introduces the field of green software engineering, showing the Green Software Foundation’s Software Carbon Intensity Specification, which is used to estimate the carbon footprint of software, and discusses ideas on how to make machine learning greener. It aims to give you the tools to take an active part in the climate solution.
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Moving towards a Future of Testing in the Metaverse
In this article, Tariq King describes the metaverse concept, discusses its key engineering challenges and quality concerns, and then walks through recent technological advances in AI and software testing that are helping to mitigate these challenges. To wrap up, he shares some of his thoughts on the role of software testers as we move towards a future of testing in the metaverse.
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How I Contributed as a Tester to a Machine Learning System: Opportunities, Challenges and Learnings
Have you ever wondered about systems based on machine learning? In those cases, testing takes a backseat. And even if testing is done, it’s done mostly by developers themselves. A tester’s role is not clearly portrayed. Testers usually struggle to understand ML-based systems and explore what contributions they can make. This is a journey of assuring quality of ML-based systems as a tester.
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Understanding and Debugging Deep Learning Models: Exploring AI Interpretability Methods
ML interpretability refers to a user's ability to explain decisions made by an ML system. Interpretability increases confidence in the model, reduces bias, and ensures that model is compliant and ethical. In this article, author Andrew Hoblitzell discusses several methods of ML interpretability and dives deep into Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Values.
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Software Testing, Artificial Intelligence and Machine Learning Trends in 2023
Technology has taken significant leaps within the last few years, introducing advancements that have taken us further into the digital age, impacting the software testing industry, and we're seeing advances in machine learning, artificial intelligence, and the neural networks making them possible. These new technologies will change how software is developed and tested like never before.
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InfoQ Software Trends Report: Major Trends in 2022 and What to Watch for in 2023
2022 was another year of significant technological innovations and trends in the software industry and communities. The InfoQ podcast co-hosts met last month to discuss the major trends from 2022, and what to watch for in 2023. This article is a summary of the 2022 software trends podcast.
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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.