Redis is the world's fastest data platform. We provide cloud and on-prem solutions for caching, vector search, and more that seamlessly fit into any tech stack. With fast setup and fast support, we make it simple for digital customers to build, scale, and deploy the fast apps our world runs on.
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Redis reposted this
Vector search is cool. Really cool. And, we're all itching for reasons to use it. And there are a lot of really good reasons. But there are some, well, not so good ones as well. Know the difference and don't let vector search become the latest Golden Hammer in your toolbox. https://lnkd.in/giDrS6Tc
Ever wonder how AI apps make sense of images, text, or video? It starts with a vector database. From vectorization to HNSW indexing, Brian Sam-Bodden, Redis’ Principal Applied AI Engineer, reveals how they work, why they matter, and how techniques like cosine similarity, HNSW indexing, and vectorization power smarter search and recommendations. Dive in: https://lnkd.in/epKu3unB
AI’s impact isn’t just about its capabilities. It also comes from the people who bring AI to life. Our VP of AI Product Management, Manvinder Singh, recently shared practical advice on turning AI potential into real-world results alongside other tech leaders. Read the full article for more: https://lnkd.in/emF7BAGT
We’re co-hosting a Founder Game Night next Thursday, July 24, in Brooklyn with Tavily, Eden, and Jake Z. at Fractal Tech. Come for an evening of board, card, and social games. Stay for the great conversations with other folks building in AI infrastructure. Bring your A-game. We’ll bring the drinks and snacks. Register here: https://lnkd.in/ew-gJNae
Seriously. He even mentioned changing our name to rAdIs. Ok, that's not true, but he really does want you to go here: https://lnkd.in/gPzeK6qn
CMO. Expert in igniting customer-led growth. I amplify brands and accelerate business growth, creating high-performing teams, award-winning brands, and product-market fitness. Ex Verizon, Yahoo, Microsoft.
Retrieval Augmented Generation (RAG) is one of the most powerful architectural patterns in GenAI today—combining the strengths of LLMs with real-time, external context from your own data. Learn more about the how and why of RAG with Brian Sam-Bodden, Principal Applied AI Engineer at Redis, including: ▶️ Query rewriting, dense retrieval, and semantic chunking ▶️ How to structure your data for better grounding ▶️ What’s happening behind the scenes ▶️ Why RAG improves accuracy, reduces hallucinations & keeps outputs fresh Check out the full video for more: https://bit.ly/3IwN2sp
If you're working with vector search, LLM pipelines, or unstructured data, you’re relying on embedding models. Redis Developer Advocate Raphael De Lio breaks down: ✅ What embedding models are and how they represent meaning using vectors ✅ How they help computers understand text, images, and unstructured data ✅ Why embeddings drive use cases like search, recommendations, and fraud detection ✅ Where to find ready-to-use models and how to get started quickly Watch the full video: https://bit.ly/4nJn4lP
Redis reposted this
Traditional search matches words. Semantic search matches meaning. Visual search matches images based on visual similarity. In this video, I explain how visual search uses image embeddings and vector similarity search to understand what an image represents, not just how it looks at the pixel level — going far beyond raw pixel comparison. https://lnkd.in/e9VP2ENh
When 100M+ fans stream live Indian Premier League matches on JioCinema, performance can’t falter. That’s why they use Redis to power real-time features like leaderboards, live view tracking, and interactive stickers, all with sub-millisecond latency. See how it works: https://lnkd.in/gUUt6yFt
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Join us in San Francisco on Sept. 4 for Redis Released, a one-day conference for teams building fast, scalable AI infrastructure. You’ll work shoulder-to-shoulder with Redis engineers, alongside experts from leading AI companies, to learn how the fastest teams are building and scaling AI systems today and preparing for what’s coming tomorrow. 🔍 Here’s a session preview worth showing up for: Ricardo Ferreira, our Developer Advocacy Lead, will share real-world strategies for keeping vector embeddings fresh in production. You’ll learn: ▶️ How to implement real-time vector synchronization at scale ▶️ Change detection strategies to avoid unnecessary reprocessing ▶️ Event-driven design patterns that keep your AI features consistent and fresh ▶️ Resilient architectural approaches that decouple vectors from shifting data models ▶️ Practical advice for developers, team leads, and architects moving from AI theory to execution Come build with us. Register here: https://bit.ly/4ktRF41