Redis at MLOps World, Redis Released, SF Tech Week, Agentic AI Workshop

Big news from the Redis crew! We’re welcoming the Featureform team to the family. This is a huge step in bringing real-time structure and orchestration to AI and ML workloads. 🔧 Featureform + Redis = next-level AI orchestration: Featureform is all about defining, managing, and reusing features across training and production. Now, with Redis, you can pair structured data and vector embeddings in one powerful, lightning-fast platform. 🚀 Why this matters • No more patchwork pipelines or extra glue code • Define your features once and use them everywhere • Keep your models accurate and accountable with better observability and governance • Blend structured data with embeddings to build smarter, faster AI systems 📦 What this means for builders If you’re creating AI agents, ML systems, or real-time applications, this combo hits all the right notes. You get simplicity, speed, and scale all in one place. Let’s elevate what’s possible together. https://lnkd.in/ewJJzpna

Today's big news for AI/ML teams — and Redis customers! Redis has acquired Featureform, creators of the popular open-source & Iceberg-native feature store platform. With Redis + Featureform, we’re now offering the industry’s only end-to-end data & ML platform that combines both a feature store & an online store -grounded in open source & Apache Iceberg. For Redis customers, this means: - Seamless real-time and batch feature serving - Simpler data pipelines from model training to production - Faster experimentation and iteration for AI/ML workloads This is a huge step, making Redis the platform for real-time, AI applications - from feature engineering to inference. https://lnkd.in/eHS_GgTE #MachineLearning #MLOps #DataInfrastructure #Redis #Featureform

Almost a terabyte of soil data indexed in Elastic At #SoilHive, we use Elasticsearch to power global access to #soil datasets — hundreds of thousands of #data points and more than a thousand raster layers, all searchable by property, dataset, and location. Our multi-zone Elasticsearch cluster on Amazon Web Services (AWS) keeps queries fast and scalable, serving researchers and developers who are building the next generation of tools to support #soilhealth. Access it through our web app https://lnkd.in/dBfRDXZ2 or open API https://lnkd.in/dz-tp9n2

AI agents are only as powerful as their memory. Redis has been named the #1 data storage choice for AI agents in 2025. The reason? Redis delivers what AI truly needs: - Real-time vector storage - Semantic caching - Long-term memory for LLMs At Bassirah, we see this as validation of why Redis is central to the next generation of AI-native data platforms powering everything from agentic RAG systems to enterprise decision engines. 🔗 Read more: https://lnkd.in/db47k6zG #AI #Redis #RAG #LLM #DataEngineering #AIAgents #ArtificialIntelligence

🚀 Great read: Simon (Xi) Ouyang's new post breaks down how to build a production-ready healthcare RAG system using Weights & Biases Evals + Redis Stack. It highlights how high-stakes domains like healthcare demand traceable, grounded answers with strict citation rules and continuous monitoring. The stack combines Redis’s hybrid vector search for sub-second retrieval with W&B’s eval suite to track grounding, citation, and latency metrics in CI/CD. The result: a modular, safety-first RAG pipeline (built with LangGraph) that’s fast, auditable, and measurable — a strong blueprint for any regulated or mission-critical AI application. Check it out here: https://lnkd.in/e52BCSck

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐝𝐨𝐧’𝐭 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐰𝐞𝐚𝐤 𝐋𝐋𝐌𝐬. 𝐓𝐡𝐞𝐲 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐰𝐞𝐚𝐤 𝐦𝐞𝐦𝐨𝐫𝐲. When I started building AI agents, I assumed model performance was everything. But the real challenge was storing and recalling context — user data, preferences, tools, and conversation history — without slowing everything down. That’s why the recent Redis 2025 report caught my eye. According to the latest Stack Overflow Developer Survey, 43% of developers building AI agents use Redis — ranking it #1 for AI agent data storage, ahead of GitHub MCP, Supabase, ChromaDB, and pgvector. And it makes sense. Redis has quietly evolved from a cache to a high-performance memory layer for AI: - 𝐔𝐥𝐭𝐫𝐚-𝐟𝐚𝐬𝐭 𝐯𝐞𝐜𝐭𝐨𝐫 𝐬𝐞𝐚𝐫𝐜𝐡 𝐰𝐢𝐭𝐡 𝐑𝐞𝐝𝐢𝐬𝐕𝐋 𝐟𝐨𝐫 𝐑𝐀𝐆 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬 - 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐜𝐚𝐜𝐡𝐢𝐧𝐠 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐫𝐞𝐝𝐮𝐜𝐞 𝐋𝐋𝐌 𝐜𝐨𝐬𝐭𝐬 𝐛𝐲 𝐧𝐞𝐚𝐫𝐥𝐲 𝟗𝟎% - 𝐇𝐲𝐛𝐫𝐢𝐝 𝐬𝐭𝐨𝐫𝐚𝐠𝐞 𝐭𝐡𝐚𝐭 𝐡𝐚𝐧𝐝𝐥𝐞𝐬 𝐛𝐨𝐭𝐡 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐚𝐧𝐝 𝐮𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐝𝐚𝐭𝐚 𝐬𝐞𝐚𝐦𝐥𝐞𝐬𝐬𝐥𝐲 Relevance AI even reported vector search latency dropping from 2 seconds to 10 milliseconds after moving to Redis. 𝐓𝐡𝐞 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲 𝐟𝐨𝐫 𝐦𝐞: In 2025, Redis isn’t just speeding up APIs — it’s powering the next generation of memory-centric AI agents. Are you still treating Redis as just a cache, or have you started using it as your AI memory backbone? Follow Gowtham SB 🎥𝐘𝐨𝐮𝐓𝐮𝐛𝐞 - https://lnkd.in/db-XNeP9 🎥𝐘𝐨𝐮𝐓𝐮𝐛𝐞 - https://lnkd.in/gc-8rdjM 𝐌𝐲 𝐁𝐨𝐨𝐤𝐬 & 𝐆𝐮𝐢𝐝𝐞 - https://lnkd.in/ggMJgs_k #DataEngineering #AgenticAI #Redis #VectorSearch #AI #DataArchitecture #RAG #MachineLearning #AIInfrastructure

Redis acquired Featureform to make it even easier for developers to deliver real-time, structured data into AI agents and applications. This brings together Redis’ speed and scalability with Featureform’s feature store technology. A huge step forward for anyone building production-grade AI systems that rely on fast, reliable data. Read more here: https://lnkd.in/eYzQc5KC

Pillsbury advised Featureform, a leading framework for managing and orchestrating structured data signals, on its acquisition by Redis, the world’s fastest data platform. The combination enhances Redis’ #AI infrastructure, enabling developers to deliver structured data to models faster, more reliably and with full observability. Click here to learn more: https://bit.ly/46UjyPc

TiKV is an open source, distributed and transactional key-value database. Growing applications demand consistent performance, and unexpected write latency spikes, especially during Sorted String Table file ingestion, were hurting predictability in TiKV. By Jinpeng Zhang and Wilco Huang, thanks to Cloud Native Computing Foundation (CNCF) and TiDB, powered by PingCAP

📣 New White Paper Alert 📣 🔓Unlock 1000x performance boost querying parquet files with this latest whitepaper, coauthored by engineers at Salesforce and Alluxio 🚀Discover how Alluxio's distributed caching can supercharge your queries on data lake with low latency and just a fraction of the cost compared to AWS S3 Express One Zone. Read Now: https://lnkd.in/gMfbzMS4 #AI #MachineLearning #DeepLearning #S3 #S3Express #RAG #LLM #Query #DataLake #Storage #Compute