InfoQ Homepage Machine Learning Content on InfoQ
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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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Going beyond the Case of Black Box AutoML
Kiran Kate covers the basics of AutoML and then presents Lale (https://github.com/IBM/lale), an open-source scikit-learn compatible AutoML library which implements Gradual AutoML.
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Simplifying Real-Time ML Pipelines with Quix Streams
Tomáš Neubauer discusses Quix Streams, an open-source Python library that helps data scientists and ML engineers to build real-time ML pipelines.
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Improve Feature Freshness in Large Scale ML Data Processing
Zhongliang Liang covers the impact of feature freshness on model performance, discussing various strategies and techniques that can be used to improve feature freshness.
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Needle in a 930M Member Haystack: People Search AI @LinkedIn
Mathew Teoh explores how LinkedIn's People Search system uses ML to surface the right person that you're looking for.
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PostgresML: Leveraging Postgres as a Vector Database for AI
Montana Low provides an understanding of how Postgres can be used as a vector database for AI and how it can be integrated into your existing application stack.
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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.