Build a high-performance feature store with Feast! 🚀 Our new guide shows you how to use @duckdb for seamless historical feature retrieval and Dragonfly for low-latency online feature serving. Code examples inside! #FeatureStore #ML #MLOps hubs.la/Q03Hz2180

For data engineers, moving beyond Spark's micro-batch limitations for true real-time processing is a common challenge. Engineers from NAVER's ADVoost Shopping team share a fantastic deep dive on how they built a faster, more reliable AI data pipeline using #ApacheFlink and #ApachePaimon, and detailed performance benchmarks: Apache Paimon vs. Apache Iceberg. They detail their architecture for selecting effective ads in real-time and loading them into a feature store for their AI models. A great read for anyone interested in high-performance, real-time data systems. 🇰🇷 Please note: The article is from Naver's official tech blog and is written in Korean. The architectural diagrams are universal, and browser translation effectively understands the key insights. Read their journey here: d2.naver.com/helloworld/2766… and watch it on YouTube: youtu.be/rrdnDwga_Ng?si=gsII… #DataEngineering #ApacheFlink #ApachePaimon #RealTimeData #Streaming #AI #FeatureStore #BigData @naver_d2

データサイエンスの効率性上げるためにFeatureStoreを社内リリースした話だけど、実質 #Databricks の使い方事例な記事です笑 特徴量を運用・公開する仕組みを社内にリリースしました！ - CCCMKホールディングス TECH LABの Tech Blog techblog.cccmkhd.co.jp/entry…

🚀 Feast Kubeflow? The Feast maintainers are proposing to donate Feast to Kubeflow! Join the discussion & share your thoughts! 👇 github.com/kubeflow/communit… Link to original post: groups.google.com/g/kubeflow… #Kubeflow #Feast #MLOps #AI #MachineLearning #GenerativeAI #FeatureStore

Which is the best managed feature store provider available to serve high traffic and real time traffic prediction. Please comment below. #FeatureStore #SaaS #MachineLearning #AI

Awesome Production Machine Learning curates production-ready ML tools across the entire ML lifecycle. It covers crucial MLOps components - from model serving to monitoring infrastructure. The repo is particularly strong in documenting tools for deployment pipelines, feature stores, and model monitoring solutions. You'll find battle-tested frameworks for distributed training, model versioning, and security hardening. Think of it as your technical compass for building enterprise ML systems. Most tools listed support RESTful APIs and container-based deployments, perfect for microservices architectures. github: /EthicalML/awesome-production-machine-learning #MLOps #MachineLearning #DevOps #ModelServing #FeatureStore #ModelMonitoring #DataEngineering #ProductionML

We described two Feature Store data model alternatives. feature-store.scylladb.com @Medium use a third one :) #scylladb #ml #FeatureStore

The Feature Store in gm.ai is a core repository for ML data, enabling processing, management, and serving of feature data for training and inference. Key features 👇👇 #gm_dot_ai #featurestore #solana #memecoins #AI

🌟 Want to know how to build feature stores with RisingWave? 🌊 Xinhao Xu will take you through all the deployment steps of this simple demo! 📘For more: risingwave.com/blog/build-fe… #ml #machinelearning #ai #featurestore

Scaling an ML Feature Store From 1M to 1B Features per Second thenewstack.io/scaling-an-ml… @c_a_dunlop @scylladb #Sponsored #MachineLearning #FeatureStore

This coming thursday, I will be participating in the #belgian #mlops #meetup and talking about how the @hopsworks #featurestore can be used as #ai / #ml  system infrastructure. Join us if you can! meetup.com/mlops-belgium/eve…

Happy to say that today marks my 6-month anniversary at @hopsworks . To celebrate, I wanted to share this short clip that our team created a while ago, but that was hidden too deep in our treasure trove of #ai, #ml, #mlops and #featurestore content: youtu.be/lMXM8lsEimY

That's why I wrote this article, and built a spreadsheet ffs :) - to help people make better #ValueCases for their #featurestore #ml #ai platform implementations.  Read it over here: hopsworks.ai/post/build-your… .

Join us for the #live session on Accelerating Price Elasticity #AI Use Cases to Production in 3–4 Weeks with Predictability and Scale: bit.ly/unifyaiwebinar #AIChallenges #MachineLearning #UnifyAI #featureengineering #featurestore #aiandml #ai #machinelearning #mlops #data

@NavarroRoberto_ nos explica el nuevo repositorio de Azure, AzureML FeatureStore 💪 #GlobalAI2024

### Vertex AI products: From about 10:15 AM to 11:35 AM US/Pacific, all Vertex AI services that heavily rely on metadata store operations including Online Prediction, Training, and Featurestore, ML Metadata and Notebooks experienced ~50% error rates (spiking to near 100% 22/65

### Vertex AI products: From about 10:15 AM to 11:35 AM US/Pacific, all Vertex AI services that heavily rely on metadata store operations including Online Prediction, Training, and Featurestore, ML Metadata and Notebooks experienced ~50% error rates (spiking to near 100% 22/65

1. WHY should I consider a #featurestore. 2. WHY should I BUY, and not BUILD a #featurestore for my #MLOps infrastructure. 3. WHY should I, if I decide to BUY instead of building that piece of infrastructure, choose @hopsworks and nothing else.

Tonight, me and @SirOibaf are enjoying some quality time briefing the lovely folks at @VectrConsulting about the advantage of using a @hopsworks #featurestore for #machinelearning. Thanks @_TomMichiels_, @Jweiren and @ignazw for inviting us!

* and then finally articulating that you should compare the #online #offline #featurestore in #ai/#ml to the #database #datawarehouse combo in #applications/#analytics: