LinkedIn post, in case you wanna share: www.linkedin.com/posts/jose-b...
#dataday #mlops #python #monterrey #méxico #machinelearning #mlengineering #datascience #dataengineering #featurestore #feast #mageai #duckdb #timeseries #featureengineering #dataday #monterrey… | Jos...
Hola red, espero que hayan tenido un bonito fin de semana! 😃
Les vengo a invitar al taller práctico que impartiremos el próximo mes en el #DataDay sobre feature stores y MLOps. Esperamos que les ayud...
www.linkedin.com
September 1, 2025 at 2:10 PM
LinkedIn post, in case you wanna share: www.linkedin.com/posts/jose-b...
GCP Vertex AIの特徴量（featurestore）の削除方法
https://tech.ldas.jp/ja/posts/how-to-delete-a-feature-store-in-gcp-vertex-ai/
#ai #gcp #vertex
https://tech.ldas.jp/ja/posts/how-to-delete-a-feature-store-in-gcp-vertex-ai/
#ai #gcp #vertex
GCP Vertex AIの特徴量（featurestore）の削除方法
GCP Vertex AIの特徴量（featurestore）の削除方法
tech.ldas.jp
June 25, 2026 at 9:04 AM
GCP Vertex AIの特徴量（featurestore）の削除方法
https://tech.ldas.jp/ja/posts/how-to-delete-a-feature-store-in-gcp-vertex-ai/
#ai #gcp #vertex
https://tech.ldas.jp/ja/posts/how-to-delete-a-feature-store-in-gcp-vertex-ai/
#ai #gcp #vertex
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
marcduerst.com/blog/machine...
#machinelearning #softwareengineering #datascience #architecture #featurestore
...and I re-launched my blog as a modern static generated page and went away from WordPress
marcduerst.com/blog/machine...
#machinelearning #softwareengineering #datascience #architecture #featurestore
LinkedIn
This link will take you to a page that’s not on LinkedIn
lnkd.in
April 3, 2026 at 9:00 PM
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
marcduerst.com/blog/machine...
#machinelearning #softwareengineering #datascience #architecture #featurestore
...and I re-launched my blog as a modern static generated page and went away from WordPress
marcduerst.com/blog/machine...
#machinelearning #softwareengineering #datascience #architecture #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
https://semyonsinchenko.github.io/ssinchenko/post/effective_feature_store_pyspark/
https://semyonsinchenko.github.io/ssinchenko/post/effective_feature_store_pyspark/
April 15, 2024 at 1:45 PM
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
https://semyonsinchenko.github.io/ssinchenko/post/effective_feature_store_pyspark/
https://semyonsinchenko.github.io/ssinchenko/post/effective_feature_store_pyspark/
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
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.
aerospike.com
July 28, 2025 at 6:09 PM
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
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
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.
aerospike.com
October 26, 2025 at 5:03 PM
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
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
A critical Feast Feature Server flaw (CVE-2026-23537) lets unauthenticated attackers perform arbitrary file write and risk remote code execution. Act now.
#Feast #FeatureStore #MachineLearning #ArbitraryFileWrite #RCE #CVE202623537 #CyberSecurity
#Feast #FeatureStore #MachineLearning #ArbitraryFileWrite #RCE #CVE202623537 #CyberSecurity
Feast Feature Server Flaw Lets Unauthenticated Attackers Write Files
A critical flaw in the Feast Feature Server carries a CVSS score of 9.1. The bug, tracked as CVE-2026-23537, sits in the /save-document endpoint. It lets an unauthenticated attacker write arbitrary JSON files to disk. Why This Feast Feature Server Flaw Matters Feast is a widely used open-source feature store for machine learning. Teams rely on it to serve data for model training and online inference.
securityonline.info
July 9, 2026 at 1:34 PM
A critical Feast Feature Server flaw (CVE-2026-23537) lets unauthenticated attackers perform arbitrary file write and risk remote code execution. Act now.
#Feast #FeatureStore #MachineLearning #ArbitraryFileWrite #RCE #CVE202623537 #CyberSecurity
#Feast #FeatureStore #MachineLearning #ArbitraryFileWrite #RCE #CVE202623537 #CyberSecurity
🚀 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
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
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...
github.com
February 4, 2025 at 12:49 PM
🚀 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
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
Feast: The open source feature store for AI
www.redhat.com/en/blog/feas...
#RedHat #OpenSource #Feast #FeatureStore #AI #DataScience
www.redhat.com/en/blog/feas...
#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.
www.redhat.com
May 18, 2025 at 1:13 AM
Feast: The open source feature store for AI
www.redhat.com/en/blog/feas...
#RedHat #OpenSource #Feast #FeatureStore #AI #DataScience
www.redhat.com/en/blog/feas...
#RedHat #OpenSource #Feast #FeatureStore #AI #DataScience
Feast: The open source feature store for AI
https://www.redhat.com/en/blog/feast-open-source-feature-store-ai
#redhat #opensource #feast #featurestore #ai #datascience
https://www.redhat.com/en/blog/feast-open-source-feature-store-ai
#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.
www.redhat.com
May 18, 2025 at 1:14 AM
Feast: The open source feature store for AI
https://www.redhat.com/en/blog/feast-open-source-feature-store-ai
#redhat #opensource #feast #featurestore #ai #datascience
https://www.redhat.com/en/blog/feast-open-source-feature-store-ai
#redhat #opensource #feast #featurestore #ai #datascience
AWS CLI 2.36.38
api-change:sagemaker-featurestore-runtime: Amazon SageMaker Feature Store now supports the UpdateRecord API, enabling partial updates to individual feature values in an existing Online Store record without rewriting the entire record. This reduces write payloads and latency for…
api-change:sagemaker-featurestore-runtime: Amazon SageMaker Feature Store now supports the UpdateRecord API, enabling partial updates to individual feature values in an existing Online Store record without rewriting the entire record. This reduces write payloads and latency for…
AWS CLI 2.36.38
api-change:sagemaker-featurestore-runtime: Amazon SageMaker Feature Store now supports the UpdateRecord API, enabling partial updates to individual feature values in an existing Online Store record without rewriting the entire record. This reduces write payloads and latency for high-frequency…
whatsnew.fyi
September 3, 2026 at 4:03 AM
AWS CLI 2.36.38
api-change:sagemaker-featurestore-runtime: Amazon SageMaker Feature Store now supports the UpdateRecord API, enabling partial updates to individual feature values in an existing Online Store record without rewriting the entire record. This reduces write payloads and latency for…
api-change:sagemaker-featurestore-runtime: Amazon SageMaker Feature Store now supports the UpdateRecord API, enabling partial updates to individual feature values in an existing Online Store record without rewriting the entire record. This reduces write payloads and latency for…
Blueprint for Building a Scalable Feature Store
https://beefed.ai/en/build-scalable-feature-store
#FeatureStore #OfflineStore #OnlineStore #FeatureIngestion #PointintimeJoin
https://beefed.ai/en/build-scalable-feature-store
#FeatureStore #OfflineStore #OnlineStore #FeatureIngestion #PointintimeJoin
Blueprint for Building a Scalable Feature Store
beefed.ai
June 2, 2026 at 9:16 PM
Blueprint for Building a Scalable Feature Store
https://beefed.ai/en/build-scalable-feature-store
#FeatureStore #OfflineStore #OnlineStore #FeatureIngestion #PointintimeJoin
https://beefed.ai/en/build-scalable-feature-store
#FeatureStore #OfflineStore #OnlineStore #FeatureIngestion #PointintimeJoin
📊 Cuando el modelo de datos frena tu sistema: lecciones de Medium
https://thenewstack.io/medium-scylladb-feature-store/
#DataModel #FeatureStore #Medium #TechScaling
https://thenewstack.io/medium-scylladb-feature-store/
#DataModel #FeatureStore #Medium #TechScaling
June 9, 2026 at 3:24 PM
📊 Cuando el modelo de datos frena tu sistema: lecciones de Medium
https://thenewstack.io/medium-scylladb-feature-store/
#DataModel #FeatureStore #Medium #TechScaling
https://thenewstack.io/medium-scylladb-feature-store/
#DataModel #FeatureStore #Medium #TechScaling
Rokt built their own feature store... find out why with Avinash Idnani:
youtu.be/qOPhghaZf-w
#dataengineering #featurestore #dataen
youtu.be/qOPhghaZf-w
#dataengineering #featurestore #dataen
December 27, 2025 at 4:19 AM
Rokt built their own feature store... find out why with Avinash Idnani:
youtu.be/qOPhghaZf-w
#dataengineering #featurestore #dataen
youtu.be/qOPhghaZf-w
#dataengineering #featurestore #dataen
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% at times) in the [16,54]
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% at times) in the [16,54]
February 28, 2024 at 5:10 PM
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% at times) in the [16,54]
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% at times) in the [16,54]
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% at times) in the region.
Google [16,50]
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% at times) in the region.
Google [16,50]
February 21, 2024 at 9:50 PM
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% at times) in the region.
Google [16,50]
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% at times) in the region.
Google [16,50]
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
See how #Aerospike supports this at scale:
https://monkeylink.co/7ae840
#FeatureStore #MachineLearning #RealTimeAI
January 30, 2026 at 5:00 PM
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
See how #Aerospike supports this at scale:
https://monkeylink.co/7ae840
#FeatureStore #MachineLearning #RealTimeAI
🆕 Feature Stores Are Overengineered: When SQL Is Enough
#MLOps #MLEngineering #FeatureStore
https://tildalice.io/feature-stores-overengineered-when-sql-enough/
#MLOps #MLEngineering #FeatureStore
https://tildalice.io/feature-stores-overengineered-when-sql-enough/
February 24, 2026 at 3:05 PM
🆕 Feature Stores Are Overengineered: When SQL Is Enough
#MLOps #MLEngineering #FeatureStore
https://tildalice.io/feature-stores-overengineered-when-sql-enough/
#MLOps #MLEngineering #FeatureStore
https://tildalice.io/feature-stores-overengineered-when-sql-enough/
Watching $ADP, ADP must replatform payroll/HCM to multitenant K8s, streaming, data-mesh, featurestore MLOps+GPU inference. Market misprices; +40% rerate. analysi
January 4, 2026 at 7:07 PM
Watching $ADP, ADP must replatform payroll/HCM to multitenant K8s, streaming, data-mesh, featurestore MLOps+GPU inference. Market misprices; +40% rerate. analysi
App Hub update on July 30, 2025 https://cloud.google.com/app-hub/docs/release-notes/#July_30_2025 #googlecloud The following Vertex AI supported resources are now generally available (GA): Dataset items Featurestore containers MetadataStore instances Model resources.
July 31, 2025 at 4:34 AM
App Hub update on July 30, 2025 https://cloud.google.com/app-hub/docs/release-notes/#July_30_2025 #googlecloud The following Vertex AI supported resources are now generally available (GA): Dataset items Featurestore containers MetadataStore instances Model resources.
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
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
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.
buff.ly
May 18, 2025 at 1:19 PM
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
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
💬 Discussion fuel:
How’s your team handling feature sprawl?
Tried Iceberg/Nessie? Hot takes?
#MLOps #DataEngineering #FeatureStore
How’s your team handling feature sprawl?
Tried Iceberg/Nessie? Hot takes?
#MLOps #DataEngineering #FeatureStore
May 14, 2025 at 1:50 AM
💬 Discussion fuel:
How’s your team handling feature sprawl?
Tried Iceberg/Nessie? Hot takes?
#MLOps #DataEngineering #FeatureStore
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
💬 Curious about Feast/Tecton's potential for your ML pipeline? Drop a comment or DM!
#MachineLearning #AI #FeatureStore
February 10, 2025 at 9:49 AM
🧵 7/7
💬 Curious about Feast/Tecton's potential for your ML pipeline? Drop a comment or DM!
#MachineLearning #AI #FeatureStore
💬 Curious about Feast/Tecton's potential for your ML pipeline? Drop a comment or DM!
#MachineLearning #AI #FeatureStore