Top Tweets for #FeatureStore
🚀 Amazon SageMaker Feature Store now supports Apache Iceberg, streaming ingestion, and fine-grained access control through AWS Lake Formation. #ML #Tech #AWS #FeatureStore
https://t.co/TFG6T5i8cQ
Feature reuse is essential to scaling the impact of enterprise ML. Databricks provides centralized feature stores, reducing duplication and promoting consistency across use cases.
#FeatureStore #MLengineering #DataScience
Blogged: Integrating Machine Learning — A Software Engineer's Guide
...and I re-launched my blog as a modern static generated page and went away from WordPress
https://t.co/wXEs0HsBuE
#machinelearning #softwareengineering #datascience #architecture #featurestore
この仕組みでMLOpsパイプラインが強化され、実験から本番環境への移行が大幅に高速化！データガバナンスを保ちつつ、チーム間での「特徴量の民主化」を推進したい組織に最適なリファレンスアーキテクチャです。
#AmazonSageMaker #AWS #MachineLearning #MLOps #FeatureStore #AI
Feature stores close the training-serving skew gap. Online store serves under 10ms; offline store powers training. Do not skip this in MLOps. https://t.co/WDKoEHE1OB #FeatureStore #MLOps #DataEngineering #thegeekytales
Feature stores are the bridge between data science and production, turning models into reliable, scalable insights. From origins to key tools, they’re shaping near-real-time, repeatable AI workflows. #AI #MLOps #DataScience #FeatureStore
Feature explosion in e-commerce machine learning? It’s an infrastructure problem. In our latest blog post, we break down how Dragonfly delivers the sub-millisecond latency and massive throughput modern feature stores demand. #ML #FeatureStore
https://t.co/V85FXvRnIy
Feature stores aren’t about tooling.
They’re about consistency.
The same feature definition should be:
- Used in training
- Used in inference
- Versioned
- Monitored
Most ML issues come from feature mismatch, not models.
#MachineLearning #FeatureStore #DataEngineering
Feature stores eliminate training-serving skew with shared feature definitions. Airbnb reduced model latency from 800ms to 50ms using Zipline. https://t.co/ao7n4ufRg6 #MLOps #FeatureStore #ML #thegeekytales
If your ML pipelines are stuck on #S3 latency, see how Blackout Power Trading scales 100K+ models in 15 mins with Alluxio 👇
https://t.co/r7IZ8IMJl9
#ML #MLOps #DataEngineering #LowLatency #FeatureStore
Faster features = faster trades. Blackout Power Trading reduced query latency 37×–83× with Alluxio, unlocking massive scale and smarter decisions 💡
https://t.co/HhsHulPhTm
#ML #AI #MLOps #FeatureStore #BusinessImpact
Feature Stores in ML:
1 One source of truth for features
2 Prevent training–serving skew
3 Faster experiments via reuse
4 Versioned, governed features
5 Online + offline in sync
Great ML runs on great features.
#FeatureStore #MLOps #MachineLearning #DataEngineering #ModelOps
🚀 Feature Store in ML
Building models is only 20%.
The other 80% = managing features.
A Feature Store centralizes, versions & reuses features, ensuring training–serving consistency and faster deployments.
Backbone of scalable MLOps.
@sdeievent
#MachineLearning #FeatureStore
Feature stores should be fast. Even on S3.
How Blackout Power Trading does it with Alluxio 👇
https://t.co/PDfjRJhF9R
#AI #FeatureStore #Data #Cloud
Rokt built their own feature store... find out why with Avinash Idnani:
https://t.co/60WpISJhvH
#dataengineering #featurestore #dataen
𝐂𝐨𝐫𝐞 𝐭𝐞𝐜𝐡/𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬:
▪ Databricks Feature Store for serving features
▪ MLflow for experiments & model registry
▪ Unity Catalog for data/model governance
▪ Batch + real‑time endpoints for inference
#FeatureStore #MLflow #UnityCatalog
🎉 Our new O’Reilly book just dropped last week! Learn how to build real-time, production-ready ML systems with MLOps, LLMOps, context engineering & agentic AI.
👉 Read Chapter 1 here: https://t.co/kJ6pzKMwMG
#MLOps #LLMOps #AI #FeatureStore #Flink
🚀 New from LK-TECH Academy:
Build a Batch Feature Store on AWS (EMR + DynamoDB) 🔥
Learn how to scale, serve, and manage ML features efficiently in 2025.
👉 https://t.co/UdPj3RhO0p
#MLOps #AWS #FeatureStore
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