InfoQ Homepage Machine Learning Content on InfoQ
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Flawed ML Security: Mitigating Security Vulnerabilities in Data & Machine Learning Infrastructure with MLSecOps
Adrian Gonzalez-Martin introduces the motivations and the importance of security in data & ML infrastructure through a set of practical examples showcasing "Flawed Machine Learning Security".
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Leveraging Open-source LLMs for Production
Andrey Cheptsov discusses the practical use of open-source LLMs for real-world applications, weighing their pros and cons, highlighting advantages like privacy and cost-efficiency.
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Scale out Batch Inference with Ray
Cody Yu discusses how to build a scalable and efficient batch inference stack using Ray.
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Why Most Machine Learning Projects Fail to Reach Production and How to Beat the Odds
Wenjie Zi discusses common pitfalls that cause these failures, such as the inherent uncertainty of machine learning, misaligned optimization objectives, and skill gaps among practitioners.
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Navigating LLM Deployment: Tips, Tricks, and Techniques
Meryem Arik discusses some of the best practices in model optimization, serving and monitoring - with practical tips and real case-studies.
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Rethinking Connectivity at the Edge: Scaling Fleets of Low-Powered Devices Using NATS.io
Jeremy Saenz discusses NATS, an open-source project for services communication, and how to leverage NATS to streamline communication and fleet management for devices at the edge.
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Generative Search: Practical Advice for Retrieval Augmented Generation (RAG)
Sam Partee discusses Vector embeddings in LLMs, a tool capable of capturing the essence of unstructured data used by LLMs to gain access to a wealth of contextually relevant knowledge.
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Being a Responsible Developer in the Age of AI Hype
Justin Sheehy discusses the dramatic developments in some areas of artificial intelligence and the need for the responsible use of AI systems.
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Unpacking How Ads Ranking Works @Pinterest
Aayush Mudgal discusses social media advertising, unpacking how Pinterest harnesses the power of Deep Learning Models and big data to tailor relevant advertisements to the pinners.
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Large Language Models for Code: Exploring the Landscape, Opportunities, and Challenges
Loubna Ben Allal discusses Large Language Models (LLMs), exploring the current developments of these models, how they are trained, and how they can be leveraged with custom codebases.
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Modern Compute Stack for Scaling Large AI/ML/LLM Workloads
Jules Damji discusses which infrastructure should be used for distributed fine-tuning and training, how to scale ML workloads, how to accommodate large models, and how CPUs and GPUs can be utilized.
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Platform and Features MLEs, a Scalable and Product-Centric Approach for High Performing Data Products
Massimo Belloni discusses the lessons learnt in the last couple of years around organizing a Data Science Team and the Machine Learning Engineering efforts at Bumble Inc.
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