InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Securing Microservices: Preventing Vulnerability Traversal
Stefania Chaplin is looking at OWASP recommendations and Kubernetes best practices to find out more about how to secure microservices and reduce vulnerability traversal.
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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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How to Operationalize Transformer Models on the Edge
Cassie Breviu discusses different model deployment architectures, how to deploy with edge devices and inference in different programming languages.
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Modern Data Pipelines in AdTech—Life in the Trenches
Roksolana Diachuk discusses how to use modern data pipelines for reporting and analytics as well as the case of historical data reprocessing in AdTech.
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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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GraphQL Caching on the Edge
Max Stoiber discusses why and how to edge cache production GraphQL APIs at scale.
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Protecting User Data via Extensions on Metadata Management Tooling
Alyssa Ransbury overviews the current state of metadata management tooling, and details how Square implemented security on its data.
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Solving Data Quality Issues to Diagnose Health Symptoms with AI
Lola Priego and Jose del Pozo discuss how they improved the user input accuracy, normalized lab data using a scoring algorithm, and how this work finishes with an AI to diagnose health.
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The Unreasonable Effectiveness of Zero Shot Learning
Roland Meertens shows how one can get started deploying models without requiring any data, discussing foundational models, and examples of them, such as GPT-3 and OpenAI CLIP.
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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.
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