A feature store centralizes ML features for training and real-time inference, keeping models consistent in production. Low-latency lookups are critical for live predictions.

See how #Aerospike supports this at scale:

https://monkeylink.co/7ae840

#FeatureStore #MachineLearning #RealTimeAI

Why We Built Our Own Feature Store (and How It Scales ML at Rokt)

Rokt built their own feature store... find out why with Avinash Idnani:
youtu.be/qOPhghaZf-w

#dataengineering #featurestore #dataen

Feature Store 101: Build, Serve, and Scale ML Features | Aerospike Learn what a feature store is, why it matters for machine learning, and how to architect low-latency online and offline stores to power real-time, scalable AI with Aerospike.

A #featurestore is essential for a modern #machinelearning pipeline.

Learn how Aerospike's sub-millisecond latency, horizontal #scalability, and built-in consistency across online/offline stores delivers a production-ready feature store platform.

https://monkeylink.co/00d518

Feature Store 101: Build, Serve, and Scale ML Features | Aerospike Learn what a feature store is, why it matters for machine learning, and how to architect low-latency online and offline stores to power real-time, scalable AI with Aerospike.

A #featurestore is essential for modern #machinelearning pipelines, transforming data into reusable features. Learn how with its sub-millisecond latency, horizontal #scalability, and built-in consistency, #Aerospike delivers a production-ready platform. https://monkeylink.co/00d518

The Harsh Reality of Building a Real-time ML Feature Platform Ivan Burmistrov shares how ShareChat built their own Real-time Feature Platform serving more than 1 billion features per second, and how they managed to make it cost efficient.

ShareChat's Real-Time ML Feature Platform​

ShareChat developed a Real-time Feature Platform serving over 1 billion features per second, balancing performance with cost efficiency. ​

Learn more: buff.ly/yRB2eea

#MachineLearning #FeatureStore #RealTimeData #ShareChat

Feast: The open source feature store for AI Feast has gained traction across various industries, powering AI applications at both startups and enterprise organizations.

Feast: The open source feature store for AI

www.redhat.com/en/blog/feast-open-sourc...

#RedHat #OpenSource #Feast #FeatureStore #AI #DataScience

Feast: The open source feature store for AI Feast has gained traction across various industries, powering AI applications at both startups and enterprise organizations.

Feast: The open source feature store for AI

www.redhat.com/en/blog/feas...

#RedHat #OpenSource #Feast #FeatureStore #AI #DataScience

💬 Discussion fuel:

How’s your team handling feature sprawl?
Tried Iceberg/Nessie? Hot takes?

#MLOps #DataEngineering #FeatureStore

🧵 7/7
💬 Curious about Feast/Tecton's potential for your ML pipeline? Drop a comment or DM!

#MachineLearning #AI #FeatureStore

Proposal: Adoption of Feast to Kubeflow · Issue #804 · kubeflow/community History with Kubeflow Feast has a long history with Kubeflow, as an add-on and previously included in the manifest dating back to March of 2021. After discussing with the @feast-dev maintainers and...

🚀 Feast + Kubeflow? The Feast maintainers are proposing to donate Feast to Kubeflow!

Join the discussion & share your thoughts! 👇 github.com/kubeflow/com...

Link to original post: groups.google.com/g/kubeflow-d...

#Kubeflow #Feast #MLOps #AI #MachineLearning #GenerativeAI #FeatureStore

Check my latest blog post about how case-when can be used as an effective alternative to groupBy in #PySpark. By this, you can optimize your #ETL pipelines for #FeatureStore updates. #DataEngineering #MLOps

semyonsinchenko.github.io/ssinchenko/post/effectiv...