InfoQ Homepage Machine Learning Content on InfoQ
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Back to Basics: Scalable, Portable ML in Pure SQL
Evan Miller walks through the architecture of Eppo's portable, performant, privacy-preserving, multi-warehouse regression engine, and discusses the challenges with implementation.
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An Open Source Infrastructure for PyTorch
Mark Saroufim discusses tools and techniques to deploy PyTorch in production.
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Malignant Intelligence?
Alasdair Allen discusses the potentially ethical dilemmas, new security concerns, and open questions about the future of software development in the era of machine learning.
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Real-Time Machine Learning: Architecture and Challenges
Chip Huyen discusses the value of fresh data as well as different types of architecture and challenges of online prediction.
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A Bicycle for the (AI) Mind: GPT-4 + Tools
Sherwin Wu and Atty Eleti discuss how to use the OpenAI API to integrate large language models into your application, and extend GPT’s capabilities by connecting it to the external world via APIs.
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Unraveling Techno-Solutionism: How I Fell out of Love with “Ethical” Machine Learning
Katharine Jarmul confronts techno-solutionism, exploring ethical machine learning, which eventually led her to specialize in data privacy.
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Open Machine Learning: ML Trends in Open Science and Open Source
Omar Sanseviero discusses the trends in the ML ecosystem for Open Science and Open Source, the power of creating interactive demos using Open Source libraries and BigScience.
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The Next Decade of Software is about Climate - What is the Role of ML?
Sara Bergman introduces the field of green software engineering, showing options to estimate the carbon footprint and discussing ideas on how to make Machine Learning greener.
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Streaming-First Infrastructure for Real-Time ML
Chip Huyen discusses the state of continual learning for ML, its motivations, challenges, and possible solutions.
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What You Should Know before Deploying ML in Production
Francesca Lazzeri shares an overview of the most popular MLOps tools and best practices, and presents a set of tips and tricks useful before deploying a solution in production.
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ML Panel: "ML in Production - What's Next?"
The panelists discuss lessons learned with putting ML systems into production, what is working and what is not working, building ML teams, dealing with large datasets, governance and ethics/privacy.
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Machine Learning at the Edge
Katharine Jarmul discusses utilizing new distributed data science and machine learning models, such as federated learning, to learn from data at the edge.