🔥 We are working with @Arbetsformed to predict appropriate job postings in real time for Swedish job seekers. We help them to identify job announcements that contain discriminatory text
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The weekend is almost here!
We want to wish you a Happy Holidays and a year 2023 full of Machine Learning, Feature Stores, and maximum real-time performance 🚀
📣 'How Data Versioning Can Be Used in Machine Learning'.
When it comes to deploying various models of the same type, data versioning is crucial, and necessary because, in ML, the modifications have to be tracked, and fixed so that the items appear better.
https://t.co/8yw4xxyvmT
Richard Woolston and the team at @AFCU have transformed their MLOps stack increasing productivity 3-4x with a Python-Centric approach.
@hopsworks Feature Store allowed AFCU to get data products into production in days instead of months using legacy tech
https://t.co/NUg3UyUhxc
Stream #FSS2022 online!
Brian Seo goes over @DoorDash's feature store architecture, learnings from supporting CRDB, and what it takes to build a feature store.
Watch "Building A Feature Store For Hyper Growth" any time you want!
🚨 https://t.co/cXV7md23Ci
The first full lecture for my https://t.co/vlIrmUe08H course is out today. Learn how to turn your models into serverless ML systems that run on free platforms, like Github and @hopsworks . It's a free course and going to open up a new world of serverless machine learning.
Join our CEO @jim_dowling today at 6 PM CEST at Open Data Science Conference @odsc in his talk: 'Beyond Notebooks - Build Serverless ML Systems.'
We'll teach you how to master data science.
Check out more at ➡️ https://t.co/BhOsZi9JeS
As you know, we released Hopsworks 3.0! 🚀
A massive amount of new and improved functionalities will give your organization an edge that will keep it light years ahead of the competition.
🤔 New APIs
🐍 Python-Centric Paradigme
☁️ All clouds... and Serverless.
End-to-end ML pipelines that go from raw data to trained models neither promote feature reuse nor maintainable code. Break the monolith. Decompose your end-to-end ML pipelines into feature pipelines, training pipelines, and inference pipelines. A🧵
Crucial thoughts when we start talking about data and features store:
❗️The feature store is a data warehouse of features for machine learning (ML).
💡The feature store is a dual database: optimized for low latency, and optimized for high volume.
More? https://t.co/EWY1frZYKQ
#ciber18 6) los vecinos del Casco Antiguo de Cuenca también son importantes, por esto, dialogaremos con alguno de ellos para conocer su opinión sobre las obras que se están llevando a cabo en la calle Santa Lucía y Paseo del Huécar para la restauración de la Muralla.