Hopsworks

Overcome legacy systems with a seamless, modular and performance-driven AI Lakehouse. Build, deploy and manage models effortlessly.

[Hopsworks @hopsworks]

One script to rule them all! Test run your own Hopsworks platform on your own infrastructure in less than 30 minutes. 🏎️💨

Deploy our Kubernetes installer and try it yourself: https://t.co/YzXYM2WL4u

[Hopsworks @hopsworks]

Our O’Reilly book 📘 covers everything from MLOps & LLMOps to real-time AI pipelines and agentic AI, all with real-world examples and open-source technologies. Paris Carbone from Apache Flink shares how well it delivers.

Get a free copy here 👇 https://t.co/sVdNqrt52i

[Hopsworks @hopsworks]

The shift from the Modern Data Stack to the Coding Data Stack is here. Join our live demo next week to see how coding agents can help build ML pipelines, workflows, and dashboards in minutes without the complexity. Register: https://t.co/l0V12iB7aG

[Hopsworks @hopsworks]

🚀 What if your AI platform could build pipelines, run models & host applications in the same environment?
This is where the data stack is heading → coding-agent-powered AI platforms.

📅 Live demo on May 27 👇
https://t.co/l0V12iB7aG
#MLOps #AIInfrastructure #ClaudeCode

[Hopsworks @hopsworks]

Here’s a preview of what’s coming in Hopsworks and how coding agents are changing the way ML systems are built.

Watch the full demo from our recent webinar to see how you can build faster and better: https://t.co/xhBTqQICQA

[Hopsworks @hopsworks]

Scaling ML is hard. Doing it right isn’t. Learn how to build 🔄 batch, ⚡ real-time, and 🧠 agentic AI systems from our O’Reilly book, praised by Vinoth Chandar 👇 Get a full digital version: https://t.co/sVdNqrt52i

[Hopsworks @hopsworks]

If you're spending more time maintaining ML infrastructure than building models, something’s wrong.

Hopsworks SaaS gives you the full AI lakehousefeature store, pipelines, model serving. Free tier for your first project.

Go build 👉 https://t.co/Ul3dgZoxRB

[Hopsworks @hopsworks]

🎉 Our new O’Reilly book just dropped last week! Learn how to build real-time, production-ready ML systems with MLOps, LLMOps, context engineering & agentic AI.

👉 Read Chapter 1 here: https://t.co/kJ6pzKMwMG

#MLOps #LLMOps #AI #FeatureStore #Flink

[Hopsworks @hopsworks]

🚀Launched Today! 'Building Machine Learning Systems' by Jim Dowling; the ultimate O’Reilly guide to scaling ML in production with batch, real-time & LLM pipelines. Your one-stop handbook for modern AI systems. 📷Buy your copy now; https://t.co/DZPW8WiPWn

[Hopsworks @hopsworks]

Hi, Everyone!

The countdown has started; this year's Feature Store Summit is just a couple of hours away. We hope you're as excited as we are!
Don’t forget to join the event through Zoom: https://t.co/3qo7afcdTY
See you soon 🙂!

[Pau Labarta Bajo @paulabartabajo_]

MLOps becomes way simpler when you realize every ML system is made of 3 pipelines ⬇️

1️⃣ The 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 prepares the data and makes it available to your models
2️⃣ The 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 re-trains the model as needed
3️⃣ The 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 makes your model predictions available to downstream apps or users.

The first person I heard talking about the 3-pipeline design is @jim_dowling , CEO and co-founder at @hopsworks .

And he happens to be the keynote speaker at the upcoming

𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗦𝘁𝗼𝗿𝗲 𝗦𝘂𝗺𝗺𝗶𝘁 𝟮𝟬𝟮𝟱 📢

𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗦𝘁𝗼𝗿𝗲 𝗦𝘂𝗺𝗺𝗶𝘁?
The Feature Store Summit 2025 is a
> 𝗙𝗥𝗘𝗘
> 100% online event
> that will take place on October 14th

where you will learn from from top companies like @Uber , @Pinterest , @hopsworks , @coinbase or @lyft how to implement ML/MLOps solutions for 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀.

This year the focus is on
> Real-Time Data for AI
> External Data for LLMs and
> Lakehouse Data for AI

No marketing.
Just engineering that works in production.

Click below to 𝗿𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 ↓
https://t.co/0cYuc8Ik6S

[Hopsworks @hopsworks]

🚀 1,600+ registrations, 13+ sessions — free & online.
Feature Stores + Real-Time AI with Uber, Lyft, Pinterest, Zalando & more.
Register: https://t.co/A6Zc42hbAk
Can’t join live? Just register to get all recordings.

[Hopsworks @hopsworks]

Hello everyone!
We’re excited to announce the 5th edition of the Feature Store Summit bringing 10+ of the world’s leading engineering teams to share how they build infrastructure for AI, ML, and real-time systems. Register now for free: https://t.co/A6Zc42gDKM

[Hopsworks @hopsworks]

Europe’s AI Vision Is Sovereignty!
GDPR and the EU AI Act have made data sovereignty non-negotiable; redefining how organizations approach AI.
Our Sovereign AI Report reveals how geography is shaping global strategy
Get your copy today! Download: https://t.co/wKKhL04pte

[Hopsworks @hopsworks]

Sovereign AI has become a boardroom priority.
69% of global organizations are making AI sovereignty a strategic focus.
📘 Get the Sovereign AI Blueprint — your guide to building regulation-ready, responsible AI.
🔗 https://t.co/wKKhL03RDG
Lead the shift. Own your AI future.

[Hopsworks @hopsworks]

🚀 We’ve upgraded to Hopsworks 4.4!
Run key data services outside your Kubernetes cluster for greater flexibility. Now with a Python API for Alerts + key bug fixes!

Read more at: https://t.co/BUVoe6R1AJ

[Hopsworks @hopsworks]

Hopsworks 4.3 is officially out, bringing powerful upgrades to your ML workflows; including a new pipeline builder ‘Brewer’ your LLM assisted AI developer, smarter GPU scheduling, enhanced access control, and more.

Read more: https://t.co/qfrj8RGKMs

[Pau Labarta Bajo @paulabartabajo_]

Let's build a 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗠𝗟 𝘀𝘆𝘀𝘁𝗲𝗺, with Python, Rust, 𝗟𝗟𝗠𝘀 and Kubernetes.

Stepy by step ⬇️

𝗢𝘂𝗿 𝗴𝗼𝗮𝗹 🎯

Let's build an ML system that can predict crypto prices 5 minutes into the future.

The data sources we will use are
> Real time market prices
> Real time market news about blockchain, or any economic factor that can possibly impact the target metric we want to predict.

𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 📐

Let's break down our system into 4 types pipelines
- Feature pipelines (2 of them)
- Training pipeline
- Inference pipeline
- Monitoring pipeline

Let's go one by one

1️⃣ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀

Our 2 feature pipelines transform raw data into reusable ML model features, and save them in our Feature Store @hopsworks

- Feature pipeline for technical indicators
> Ingests market trades in from a websocket API like Kraken Digital Asset Exchange
> aggregates them into 1-minute windows
> Engineers technical indicators to capture momentum in the. market, and
> Pushes them to the Feature Store

- Feature pipeline for market sentiment
> Scrapes news by pooling a website like coinbase,
> Parses the raw text into a structure sentiment score using an LLM, and
> Pushes them to the Feature Store

2️⃣ 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲

We can use a boosting tree model like XGBoost to uncover any patterns between the
- current technical indicators, and
- market senttiment
and
- the price of BTC or ETH 5 minutes into the future

The final model is pushed to the model registry.

3️⃣ 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲

To serve the final predictions, while making them easily accessible to our next pipeline (the monitoring pipeline) I recommend you split this step into 2 services:

- Prediction generator, that loads the model from the registry and continuously uses the latest features to generate a new prediction and push it o a persistent storage, like an Elastic Search index.

- Prediction API. This is a lightweight service that
> Receives incoming requests from client apps,
> Finds the predictions in the Elastic Search index, and
> Returns the prediction to the client.

4️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲

There are lots of things you can monitor here. The most fundamental one is the error of your predictions.

To monitor these you need to build a streaming service that
> Listens to incoming price data
> Loads the predictions from the DB
> Computes the error and
> Pushes the error into another Elastic Search index

These errors are plotted on a dashboard using Kibana, and trigger alerts to your Slack or Discord team.

BOOM!

𝗪𝗮𝗻𝗻𝗮 𝗹𝗲𝗮𝗿𝗻 𝘄𝗶𝘁𝗵 𝗺𝗲?

On December 2nd, 212 brave students and myself will start building this system using
> Python
> Rust
> LLMs
> MLOps, and
> Kubernetes

Wanna join this adventure?
https://t.co/rBeeM1lrr0

[Hopsworks @hopsworks]

🚀 Hopsworks 4.2 is officially live! Packed with powerful updates to supercharge your ML workflows, including major improvements to the Online Feature Store, upgrades, bug fixes, and more!  

Read more at: https://t.co/47XCq08G6u

#MachineLearning #AI #Hopsworks #DataScience #FeatureStore