InfoQ Homepage Machine Learning Content on InfoQ
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Introducing the Hendrix ML Platform: an Evolution of Spotify’s ML Infrastructure
Divita Vohra and Mike Seid discuss Spotify’s newly branded platform, and share insights gained from a five-year journey building ML infrastructure.
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Strategy & Principles to Scale and Evolve MLOps @DoorDash
Hien Luu shares their approach to MLOps, and the strategy and principles that have helped them to scale and evolve their platform to support hundreds of models and billions of predictions per day.
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Declarative Machine Learning: a Flexible, Modular and Scalable Approach for Building Production ML Models
Shreya Rajpal discusses declarative ML systems, and how they address key issues that help shorten the time taken to bring ML models to production.
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LLMs in the Real World: Structuring Text with Declarative NLP
Adam Azzam discusses why building machine learning pipelines to extract structured data from unstructured text is a popular problem within an unpopular development lifecycle.
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Fabricator: End-to-End Declarative Feature Engineering Platform
Kunal Shah discusses how their ML platform designed Fabricator by integrating various open source and enterprise solutions to deliver a declarative end-to-end feature engineering framework.
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Ray: the Next Generation Compute Runtime for ML Applications
Zhe Zang introduces the basic API and architectural concepts of Ray, as well as diving deeper into some of its innovative ML use cases.
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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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