Hopsworks

One script to rule them all! Test run your own Hopsworks platform on your own infrastructure in about 30 minutes. 🏎️💨 While it’s running you have just enough time to: • Make a snack • Quick meditation break • Call a friend Deploy our Kubernetes installer and try it yourself: 

We just released the second lesson of our Hands-on H&M Real-Time Personalized Recommender course on feature pipelines that leverage MLOps best practices. In this lesson, written by Paolo Perrone , we dive deeper into building a feature pipeline for real-time personalized recommendations that tailor H&M product suggestions for users based on: - Their preferences - Their behaviors. If you're a: - Data scientist - ML engineer - Passionate about recommender systems This is your chance to get hands-on experience with a cutting-edge project. We used the Hopsworks AI Lakehouse to manage and operationalize the entire machine-learning lifecycle without worrying about infrastructure. Here's what you’ll learn in Lesson 2: - process the H&M dataset - engineer features for both the two-tower network and ranking models - create and manage Hopsworks Feature Groups for efficient ML workflow -lay the groundwork for future steps, such as integrating streaming pipelines to enable real-time data processing and recommendations. 🔗 Get started with Lesson 2 on Decoding ML → https://lnkd.in/d2J6rnqR Feel free to share your thoughts or questions in the comments below. Let’s build together! Thank you, Paolo Perrone, for writing this amazing lesson! #machinelearning #artificialintelligence #mlops #datascience

A little late to the trend, but we finally have our very own Hopsworks wrapped! Sharing a few snapshots and highlights from the past year with our platform, users and coworkers. Here’s a sample of our favorite snapshots: 🔥 Our users created more than 4000 unique projects  🤯 Half a million features were created ✅ Our engineering team resolved 1400+ tickets 🚨 Our most common typo was “enteprise” instead of “enterprise” 🎉 And of course, the release of Hopsworks 4.0 Let’s make 2025 even more memorable 💫

Looking forward to 2025, I have been working together with the Hopsworks team to try to understand what the upcoming regulations are going to mean for the Financial Services Industry (#fsi). There's a lot going on here, but I think it's really interesting and positive - and it will only increase the need for an #ai #lakehouse architecture, bringing together #mlops and #featurestores. https://lnkd.in/eTXHbwmu

Hopsworks 4.0 is a milestone, great team work has been put at making Hopsworks #kubernetes native. This blog follows the evolution of a core #security module of Hopsworks #ai #lakehouse towards a #kubernetes first-class citizen.

Lesson 1 and 2 of our course together with Paul Iusztin is out now! Learn how to use Hopsworks to build a real-time personalized recommender for H&M articles. 💡

Today, Jim Dowling is representing Hopsworks in a panel at AI STAC in London, a conference dedicated to the AI infrastructure needs of the finance industry. In the panel “Leaner AI: Integrating breakthrough capabilities without the bloat”, the experts will focus on how to optimize toolchains to both streamline app development and reduce operating costs while maintaining the quality of outputs. They will also discuss best practices for creating scalable architectures that adapt to evolving GenAI capabilities and provide high performance with minimum complexity.

A couple of weeks late on this, but I had the pleasure of presenting at the Data Engineering Meetup London at Mastercard's offices. It was a real pleasure to share insights and learn from some bright folks in data engineering. Special shoutout to Alex Merced from Dremio— top notch presentation, on point and added a lot of value. Big thanks to Inès Jacques, Chloé Caron and Chloé Roumengas from the org (super nice talk as well!) and attendees for creating such a nice atmosphere. For those curious about future events, check them out here: link; https://lnkd.in/d4ShxWSm, Looking forward to next event!

🥇 GIVEAWAY 🥇 Want a ticket to the PyData Global online conference next week? Then you’re in luck! Participate in our giveaway and get the opportunity to snag one for free. The rules of the game are simple: - Share a post showcasing a use case, project or any other cool stuff where you have leveraged Hopsworks. - Mention/tag Hopsworks in your post so we don’t miss it. - Done! We’ll pick and notify the winners on Monday to receive a ticket. Good luck!

Not only is Hopsworks 4.0 now available on our Serverless platform, but we have also teamed up with Paul Iusztin at Decoding ML to create a course on how to build a real-time personalized recommender using Hopsworks. 🎉 In a series of lessons he’ll go through how to build and deploy a real-time personalized recommender for H&M articles using Hopsworks Serverless as the feature store, model registry and serving layer. First lesson is out now! Start by getting an introduction to the architecture of the H&M recommender design and the architectural patterns necessary for building the system end-to-end. Enjoy!