Pillsbury advises Featureform on acquisition by Redis

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

There is an insightful article we stumbled upon from Redis. It explores the future of AI agent data storage in the year 2025, their message is clear: The true potential of AI agents lies not only in their intelligence but in the speed, scalability, and adaptability of the system that powers them! AI agents thrive on real-time context, retrieving, storing, and processing information instantly to make decisions. This is where Redis plays a pivotal role. - Delivering sub-millisecond latency to support real-time AI interactions  - Scaling Seamlessly as agent workloads grow - Providing unfilled platform that connects LLMs, memory, Vector search, and event-driven data These points directly align with the findings from the Stack Overflow survey of 2025 as well! where developers emphasized performance reliability and ecosystem support as critical factors in adopting technologies. Redis continues to be one of the most widely trusted and adopted tools in this space, proving that robust infrastructure is the backbone of modern AI As the AI agents evolve, the data layer is as important as the model itself, choosing the right storage solution to determine whether AI is merely functional or truly transformative. You can read more from the link below: https://lnkd.in/gZJ4QC7V

Redis — we’re everywhere. But not just in your daily life, check this out: MLOps World | Oct 8–9 | Austin Redis’ own Robert Shelton will dive into Eval-Driven Development and what it takes to build an agent from the ground up. Stop by the booth or join the talk. Redis Released | Oct 9 | London A packed lineup of innovators including Baseten, Tavily, cognee, mangoes.ai, and Entain sharing what’s next in AI and data infrastructure. SF Tech Week | Oct 10 | San Francisco A full day focused on production-ready agents — with Redis, Snowflake, Tavily, Lovable, CopilotKit, and others. Agentic AI Workshop | Oct 13 | San Francisco Hands-on sessions hosted with BeeAI and Tavily exploring how to build intelligent, connected systems.

New from O’Reilly + Redis: Managing Memory for AI Agents 📘 AI agents are only as good as their memory. This new report explores how short-term, long-term, and persistent memory shape agent performance—and how Redis provides real-time, scalable architectures for production-ready AI. 📖 Read the full report: https://lnkd.in/g7t--57p

Boost Your Local AI: Supercharge Gaia Nodes with Redis for Speed & Memory Are you running AI workloads locally using a Gaia Node? That's fantastic! Gaia Nodes let you tap into powerful, open-source Large Language Models (LLMs) right on your own hardware, offering privacy and control. But how do you make these local AI interactions faster and smarter? Enter Redis. In this post, we'll explore how combining a Gaia Node with Redis can significantly enhance your AI applications. We'll walk through two practical examples showing how Redis acts as a high-speed data engine, providing caching and memory capabilities that complement your local LLM perfectly. Gaia Nodes are excellent for running LLMs locally, providing an OpenAI-compatible API. However, even the fastest local LLM can benefit from intelligent data handling: Speed: Redis is an in-memory data store, meaning data access is incredibly fast. This is perfect for storing frequently accessed data or intermediate results. Caching: Repeatedly asking the same question? Instead of hitting your Gaia Node every tim https://lnkd.in/gRaFarsH

Boost Your Local AI: Supercharge Gaia Nodes with Redis for Speed & Memory Are you running AI workloads locally using a Gaia Node? That's fantastic! Gaia Nodes let you tap into powerful, open-source Large Language Models (LLMs) right on your own hardware, offering privacy and control. But how do you make these local AI interactions faster and smarter? Enter Redis. In this post, we'll explore how combining a Gaia Node with Redis can significantly enhance your AI applications. We'll walk through two practical examples showing how Redis acts as a high-speed data engine, providing caching and memory capabilities that complement your local LLM perfectly. Gaia Nodes are excellent for running LLMs locally, providing an OpenAI-compatible API. However, even the fastest local LLM can benefit from intelligent data handling: Speed: Redis is an in-memory data store, meaning data access is incredibly fast. This is perfect for storing frequently accessed data or intermediate results. Caching: Repeatedly asking the same question? Instead of hitting your Gaia Node every tim https://lnkd.in/gRaFarsH

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

When to use Daft for batch inference? ✅ You need to run models over your data: Express inference on a column (ie. llm_generate, embed_text, embed_image) and let Daft handle batching, concurrency, and back-pressure ✅ You have data that are large objects in cloud storage: Daft has record-setting performance when reading from and writing to S3, and provides fe ✅ You're working with multimodal data: Daft supports datatypes like images and videos, and supports the ability to define custom data sources and sinks and custom functions over this data. ✅ You want end-to-end pipelines where data sizes expand and shrink: For example, downloading images from URLs, decoding them, then embedding them; Daft streams across stages to keep memory well-behaved. How does it work? Daft provides first-class APIs for model inference. Under the hood, Daft pipelines data operations so that reading, inference, and writing overlap automatically, and is optimized for throughput. Check out our documentation for examples of using Daft for batch inference, scaling out on Ray, common patterns that we’ve discovered work well, and relevant case studies: https://lnkd.in/g9eKEE8W Try it out yourself and get started: pip install daft #Daft #Multimodal #DataEngineering #OpenSource

Databricks ai_classify: Now Available on Compute Clusters! 🚀 Senior Data Engineers, great news for your AI-powered workflows! The ai_classify function – that handy SQL tool for classifying text with generative AI models – is now supported on Databricks compute clusters. Previously limited to serverless compute, this expansion enables you to run it on your custom Runtime clusters (15.1+), providing more control over resources and costs in batch jobs. No more serverless-only constraints! 🔓 Quick example for sentiment classification on customer reviews: SELECT review_text,      ai_classify(review_text, ARRAY('positive', 'negative', 'neutral')) AS sentiment FROM customer_reviews WHERE date > '2025-01-01'; Check out the official Databricks Doc: https://lnkd.in/gqZAY9hu Who's integrating ai_classify in their clusters already? Share your use cases! 👇 #DataEngineering #Databricks #AIClassify #BigData #SparkSQL #SeniorEngineer #ETL #MachineLearning #CloudData #TechUpdate #Analytics #Lakehouse