InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Beyond the Warehouse: Why BigQuery Alone Won’t Solve Your Data Problems
Sarah Usher explains why relying solely on a data warehouse fails at scale. She shares a 3-layer data lifecycle (Raw, Curated, Use Case) to help engineering leaders build flexible, decoupled systems.
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Foundation Models for Ranking: Challenges, Successes, and Lessons Learned
Moumita Bhattacharya explains how Netflix unifies search and recommendations using the "UniCoRn" model and leverages Transformer-based foundation models to personalize the experience for 300M+ users.
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How to Unlock Insights and Enable Discovery within Petabytes of Autonomous Driving Data
Kyra Mozley explains Perception 2.0, shifting from rigid CV pipelines to semantic embeddings. She shares how Wayve uses foundation models & vector search to solve the edge case "needle in a haystack."
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How to Build a Database without a Server
Alex Seaton explains how Man Group built ArcticDB, a serverless database connecting directly to S3. He discusses using CRDTs to manage global state and leveraging immutable trees for atomicity.
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Lessons Learned from Building LinkedIn’s First Agent: Hiring Assistant
Karthik Ramgopal and Daniel Hewlett explain LinkedIn’s shift to agentic AI. They share how a modular supervisor-sub-agent architecture and a centralized skill registry power the new Hiring Assistant.
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Ecologies and Economics of Language AI in Practice
Jade Abbott explains how to build sustainable AI using "Little LMs." She discusses environmental impacts, linguistic justice, and technical optimizations like quantization and model distillation.
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Lessons Learned from Shipping AI-Powered Healthcare Products
Clara Matos shares lessons from shipping AI in healthcare at Sword Health. She discusses building guardrails, utilizing LLM-as-a-judge evals, and optimizing RAG to ensure safety and reliability.
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Powering Enterprise AI Applications with Data and Open Source Software
Francisco Javier Arceo explored Feast, the open-source feature store designed to address common data challenges in the AI/ML lifecycle, such as feature redundancy, and low-latency serving at scale.
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Securing AI Assistants: Strategies and Practices for Protecting Data
Andra Lezza reviews the OWASP Top 10 for LLMs and contrasts security controls for independent vs. integrated copilot architectures.
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Reliable Data Flows and Scalable Platforms: Tackling Key Data Challenges
Matthias Niehoff discusses bridging the gap between application and data engineering. Learn to apply software engineering best practices, embrace boring technologies, and simplify architecture.
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Humans in the Loop: Engineering Leadership in a Chaotic Industry
Michelle Brush discusses engineering leadership in the age of AI/ML and automation.
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AI-Driven Software Delivery: Leveraging Lean, ChOP & LLMs to Create More Effective Learning Experiences at QCon
Wes Reisz details building a RAG-powered QCon certification in 4 weeks. He dives into the serverless pipeline, RAG architecture, lessons on using supervised coding agents, and Lean thinking.
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