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Python-centric Feature Store

Easily build and manage feature, training and inference pipelines. Support for Python, PySpark, Spark, Flink, and SQL.

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Integrate your Data platforms and ML platforms

Hopsworks is the most effective feature platform for designing and operating feature pipelines, training pipelines, and inference pipelines. Runs out of the box with your existing pipelines, so you don’t have to redesign your pipelines to work with Hopsworks.

Data Flow with a feature store

For all teams and all data

Increase team productivity and deploy your models faster.

Richard Woolston

"Our journey with Hopsworks has been an amazing transformation that's really enabled us to be innovative and reach a point that we wouldn't have been able to reach otherwise.”

Richard Woolston

Data Science Manager - AFCU

Jump right in

Our Python SDK is designed for ease of use. You simply import the Hopsworks library in your Python or PySpark program, connect to your project and start writing data to the feature store.

It is just as easy to create training data from selected features or get inference data for batch or online applications. 

Read our docs to find out more.

# connect to Hopsworks

import hopsworks

project = hopsworks.login()
fs = project.get_feature_store()
# create a feature group

transactions_dataframe = ...

= fs.get_or_create_feature_group(    
   description="Transaction fatures 30 mins window",


Our Community

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What rhymes with Friday more than beer tasting?
New Hopsworks beers are coming... 😏
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Thank you to
@hopsworks for their Gold Sponsorship and for helping support PyData Global 2022!

Join us:

#python #data #datascience #dataanalytics #tech
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Python Ireland@PythonIreland
@jim_dowling at #PyConIe this Sunday as he discusses Data Validation for Machine Learning!

#MachineLearning #PyData
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Are you in Dublin this weekend? You are in luck. We'll be at
@PythonIreland with
@jim_dowling: 'Data Validation for ML using Great Expectations and Hopsworks'. We'll cover the concepts of Great Expectations and how you can leverage them in a feature store
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We received an incredible gift!!! Thanks to the Semantic Layer Summit,
@AtScale, for your note, and congratulations on an amazing Summit!

@jim_dowling's talk "Feature Store and the Semantic Layer" on demand!

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Discover how #FeatureStores and Model Serving platforms can be integrated to enable real-time #machinelearning applications. 

Here are the slides
@jim_dowling shared in the latest
@odsc webinar ⬇️
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Real-time Machine Learning with Hopsworks
Jim Dowling Presentation @ODSC
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merve ❤️‍🩹@mervenoyann
second workshop of the day that involves
@huggingface Spaces &
@Gradio, delivered by
@jim_dowling 🤩 check out his free course on serverless ML here 👉🏼
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🤔 Think about building your own feature store? Here are the steps you need to think about ⬇️
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We are here, and ready, at
@PyData NYC! Come and see hi 🔥
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Wanna build this system?

Then you need a serverless Feature Store, like

→ It is 100% Python 🐍
→ Great tutorials and documentation 📚
→ 10GB of free storage, so you build this project for free 😎

Click on this link to register for FREE ↓
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Hopsworks Feature Store
Hopsworks is the best python-centric feature store with the most complete MLOps capabilities. Collaborate and create an effective pipeline on all of your AI data, and bring your models to production.
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Good news to all our
@pyconse attendees!

@jim_dowling will host a webinar: 'Build ML Systems with just Python and free Serverless Services.' We will build an end-to-end ML system that predicts wave heights at a beach in only Python.
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PyCon Sweden
PyCon Sweden 2022 will take place between November 3 - 4.
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Get started with
@RonDB_ai with #DockerCompose. We created an option for creating a RonDB cluster locally using Docker Compose. 
Learn how to use this repository to create a cluster and run benchmarks on it ⬇️
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Quick Starting RonDB with Docker Compose
The goal of this tutorial is to show you how to quick start RonDB with Docker Compose and how to use the repository to create a cluster and run benchmarks
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Anytime! Watch #FSS2022! 🚀

We hosted an amazing panel moderated by
@jim_dowling from
@hopsworks. 'APIs for Feature Stores' discussed the historical evolution and the future of APIs for feature stores.

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