InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Stream All the Things — Patterns of Effective Data Stream Processing
Adi Polak shares effective data stream processing patterns, common mistakes, and exactly-once semantics.
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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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GenAI for Productivity
Mandy Gu shares Wealthsimple's journey leveraging generative AI for productivity and operational optimization.
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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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Responsible AI for FinTech
Lexy Kassan discusses responsible AI: regulation (EU AI Act, FinTech), ethical principles, governance, and FinTech's disruptive response.
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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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Rockset - Building a Modern Analytics Database on Top of RocksDB
Igor Canadi discusses building a real-time search analytics database on RocksDB, covering cloud-native design, replication, shared storage, and analytics.
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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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AI in the Age of Climate Change
Nischal HP shares insights on building a data-driven economy to incentivize sustainable farming and reduce carbon emissions.
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How GitHub Copilot Serves 400 Million Completion Requests a Day
David Cheney explains the architecture powering GitHub Copilot, detailing how they achieve sub-200ms response times for millions of daily requests.
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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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