InfoQ Homepage Machine Learning Content on InfoQ
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A Framework for Building Micro Metrics for LLM System Evaluation
Denys Linkov discusses critical lessons for senior developers and leaders on building robust LLM systems and actionable metrics that prevent production issues and drive business value.
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Supporting Diverse ML Systems at Netflix
David Berg and Romain Cledat discuss Metaflow, Netflix's ML infrastructure for diverse use cases from computer vision to recommendations.
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From "Simple" Fine-Tuning to Your Own Mixture of Expert Models Using Open-Source Models
Sebastiano Galazzo shares practical tips and mistakes in creating custom LLMs for cost-effective AI. Learn LoRA, merging, MoE & optimization.
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How Green is Green: LLMs to Understand Climate Disclosure at Scale
Leo Browning explains the journey of developing a Retrieval Augmented Generation (RAG) system at a climate-focused startup.
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LLM and Generative AI for Sensitive Data - Navigating Security, Responsibility, and Pitfalls in Highly Regulated Industries
Stefania Chaplin and Azhir Mahmood discuss responsible, secure, and explainable AI in regulated industries. Learn MLOps, legislation, and future trends.
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Unleashing Llama's Potential: CPU-Based Fine-Tuning
Anil Rajput and Rema Hariharan detail CPU-based LLM (Llama) optimization strategies for performance and TCO reduction.
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Navigating LLM Deployment: Tips, Tricks, and Techniques
Meryem Arik shares best practices for self-hosting LLMs in corporate environments, highlighting the importance of cost efficiency and performance optimization.
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The Harsh Reality of Building a Real-Time ML Feature Platform
Ivan Burmistrov shares how ShareChat built their own Real-Time Feature Platform serving more than 1 billion features per second, and how they managed to make it cost efficient.
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Recommender and Search Ranking Systems in Large Scale Real World Applications
Moumita Bhattacharya overviews the industry search and recommendations systems, goes into modeling choices, data requirements and infrastructural requirements, while highlighting challenges.
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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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