Lohit Patil

Learn how to build an image search app using a series of guided code commits with Developer Advocate Guy Royse. He’ll guide you through generating image embeddings using freely available Hugging Face models and storing them in Redis for fast, scalable semantic search.  The workshop covers: ▶️ What embeddings are and how they work ▶️ How to generate image embeddings and store them in Redis ▶️ Building a semantic search app with Redis, Python, and JavaScript Get access here: https://lnkd.in/gbZ3imbz

44

Redis 8 is GA and packed with new features, including new data types, faster commands, higher throughput, and faster replication. Learn more about our most performant and scalable version yet: https://lnkd.in/eFU5qPDs

23

1 Comment

Traditional Kafka consumers for analytics = expensive, complex, and fragile. Replay, deduplication, schema drift… it’s a lot. On Oct 9, we’ll show how Estuary + MotherDuck simplify all of this with a fresh approach to streaming ingestion. 🔗 https://lnkd.in/dncWFDGm

8

Tip: Scale large HNSW vector sets in Redis by splitting your dataset across multiple instances and querying them in parallel with multiplexing, enabling efficient parallel reads and writes that drastically improve performance and manage memory effectively. This approach leverages Redis's scalable vector support to handle hundreds of millions of vectors seamlessly. #Redis #HNSW #VectorSearch #ScalableAI #DataScience

1

Who needs self-service? In this article, Conduktor’s VP of Engineering, Matt Searle, talks about his own experiences attempting to balance developer autonomy alongside platform stability. His key insight? Govern Kafka, or risk havoc. Read his latest blog—and learn more about how Conduktor can help you implement self-service with guardrails. https://lnkd.in/ejWTRctu

14

We have a new Breez SDK update 👀 Want to implement atomic swaps on Spark? You now can with HTLC support. Check the release notes for more updates and the latest features: https://lnkd.in/euRXDVns

19

1 Comment

Kafka works great… until it’s in production. Observability in Kafka isn’t just about broker metrics. The real challenge is understanding how producers, consumers, brokers, and the apps around them behave together, without stitching disconnected signals manually. Next Thursday, we’re hosting a live session on Observing Kafka at Scale with OpenTelemetry. We’ll cover: ➡️ What to instrument (and what’s noise) ➡️ Where teams hit blind spots at scale ➡️ How to correlate Kafka signals with app & infra context ➡️ A live demo If you run Kafka in production, or are planning to, this one’s for you. 👉 RSVP here: https://lnkd.in/gnRKwkvU #kafka #opentelemetry #observability

3

The Redis AI Challenge ends in less than a week (Aug 10th deadline)! Think it's too late to join? Think again! Here are 3 project ideas that you can build in a week. Project 1: Real-Time Poll App Prompt: Beyond the Cache Build a web app where users can create polls and vote, with results updating live for everyone. A perfect project to showcase Redis for real-time applications. Redis Use: Hashes for poll data, Sorted Sets for vote counts, and Pub/Sub for live updates. Project 2: Simple Job Queue Prompt: Beyond the Cache Create a background job system. A web app submits a task (like sending an email) to a queue, and a separate worker process handles it. This is a core backend development pattern. Redis Use: Redis Lists as a simple and reliable message queue using LPUSH and RPOP. Project 3: AI-Powered Article Suggester Prompt: Real-Time AI Innovators Build a blog that recommends similar articles to the one being read. It's a fantastic, beginner-friendly entry into the world of AI. Redis Use: Use Redis as a vector database to store AI-generated embeddings of your articles and perform lightning-fast similarity searches. --- Head to the challenge page to see entry details and peep what other people are building. Can't wait to see what you build! https://lnkd.in/eH_Dk2wu #Redis #AI #VectorDatabase #MachineLearning #Backend #Programming

22

Looking for a Kubernetes-native, serverless platform to run scalable, reliable event-driven applications, including inference on streaming data workloads? 👇 Hear directly from 𝐍𝐮𝐦𝐚𝐟𝐥𝐨𝐰 PM Sriharsha Yayi, Stuff Product Manager at Intuit and Krithika Vijayakumar, Senior Software Engineer at Intuit 🔥 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐟𝐨𝐫 𝐟𝐫𝐞𝐞 𝐟𝐨𝐫 𝐭𝐡𝐢𝐬 𝐚𝐧𝐝 𝐦𝐚𝐧𝐲 𝐦𝐨𝐫𝐞 𝐠𝐫𝐞𝐚𝐭 𝐬𝐞𝐬𝐬𝐢𝐨𝐧𝐬 𝐚𝐭 https://lnkd.in/eJkr7wqS 📆 𝐓𝐮𝐞𝐬𝐝𝐚𝐲, 𝐒𝐞𝐩𝐭𝐞𝐦𝐛𝐞𝐫 𝟐𝟑𝐫𝐝, @ 𝟒 𝐩𝐦 𝐂𝐄𝐓 | 𝟏𝟎 𝐚𝐦 𝐄𝐓 | 𝟕 𝐚𝐦 𝐏𝐓 Running inference on streaming data is complex. Teams must connect to event sources, process streams, and scale inference with minimal overhead. Numaflow, a Kubernetes-native open source platform, simplifies this by allowing teams to do stream processing and inference in the same pipelines natively on Kubernetes. It integrates with Kafka, Pulsar, SQS, and more to support diverse event sources and scales to handle high-throughput workloads. Numaflow (created by the creators of Argo Project) is currently in use by large enterprises. This talk shows how Numaflow enables ML engineers to achieve scalable stream processing and inference on streaming data without the infrastructure burden. #Numaflow #Kubernetes #InferenceStreaming #EDA #DevSecOps #KubeCrash #PlatformEngineering 

30

1 Comment