Serverless ML

We are creating a community around the Infrastructure-less MLOps, without being Kubernetes experts. #MLmodels can be built and deployed without worrying about infrastructure management. Learn from your peers in this dynamic community. Join us ⬇️ bit.ly/3xDMDMJ

The wait is finally over! 🎉All sessions from the Feature Store Summit 2025 are now uploaded on YouTube. Catch the ones you missed or replay your favorites → ▶️ youtube.com/playlist?list=… #FeatureStoreSummit #MLOps #AI #FeatureStores

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: us02web.zoom.us/j/85069036571 See you soon!

🚀 Over 1,600 registrations. 13+ talks. Free. Online. Hear how Uber, Lyft, Pinterest, Zalando & more push production ML & Real-Time AI. You don't want to miss this: featurestoresummit.com/register No time that day? Register to get all the recordings.

Imagine you had to pick a novel data source and identify a prediction problem for it. What ML system could you build in a few weeks? Here's what KTH students built (mostly with @hopsworks). id2223kth.github.io/assignments/20… Examples in the 🧵below

Excited to dive into the world of real-time data for your LLMs? 🚀  LLMs' output depends on the input, so keeping it fresh is key for real-world apps like recommenders and finance.  Learn to build your real-time feature pipeline with Python and Bytewax: bit.ly/40777dM

Learn to build real-world ML apps using LLMs with this free Hands-on LLM course! 📣 This course is not about building yet another LangChain demo in a Jupyter notebook, but a complete ML app following the 3-pipeline design, and using Serverless ML tools. mtr.cool/fgmgmpxfmm

Boost your chances of landing an ML engineering job! 🤖 Create & share an open-source MLOps project to stand out. Need inspiration? Explore 20+ ML apps with source code using Serverless ML tools. Check them out: mtr.cool/bmhbezxacl

Here is a FREE hands-on course where you will learn step-by-step how to build a fully working ML product using -> MLOps best practices, -> the latest advancements in LLMs and -> a Serverless stack: CometML, Qdrant, Bytewax, Beam. mtr.cool/fvynwygdsc

Here is a free online event on Production ML. If you want to work as a professional ML engineer you need to learn how to deploy ML models in production environments. And this is what the Feature Store Summit 2023 is all about! Register for FREE today: mtr.cool/hrzzdwanpx

DuckDB has emerged as the open-source DB for fast SQL querying. 🦆 It is used to build one of the key components of an ML stack: the Feature Store. In this blog post by Hopsworks you can learn all the details: mtr.cool/vqzfltreal

Interested in learning how to fine-tune a Large Language Model? Discover all the essential ingredients you need to fine-tune LLMs for your projects in this informative article. 📚✨ mtr.cool/jmcmfeczse

LLM (as any other ML model) are as good as the input data you use to both train them, or run inference on them. Here is an open-source library that will help you clean textual data, so you can train and build better LLM apps. mtr.cool/bdyhvdaahj

Wanna learn how to build a complete ML app, from business problem to MLOps, using a Serverless stack? ⚙️ Check this out ↓  mtr.cool/cytydynyrk

Here is a hands-on tutorial on creating and deploying an ML REST API with Serverless ML tools. Code is open source and video lectures are available on YouTube! 🚀 mtr.cool/ynvvbwwznt

👉 Follow the D.R.Y principle in software engineering – implement once, use repeatedly.  Check out this post and discover modular real-time feature pipeline coding! mtr.cool/upwbkatpwf

Preparing for your first real-world ML project? ⚒️ Check out this insightful article that highlights three key things you need to know for success. my.mtr.cool/tjksdqwpmj

Learn how to develop and deploy a real-time feature pipeline in 100% Python that -> fetches real-time trade data  -> transforms trade data into OHLC data in real-time using Bytewax, and -> stores these features in the Hopsworks Feature Store my.mtr.cool/tbkiourjtq

I used to spend hours setting up IAM roles, Docker registries, and EC2 instances, to build ML products ... until I discovered this ↓

Wanna learn how to train, deploy and automate a real-time ML system? Step by step? Hands-on? In Python? for FREE? Enjoy this hands-on tutorial, and give it a star ⭐ in GitHub if you like it ↓↓↓ my.mtr.cool/nhqtpsvpdm

This week we want to share an excellent ML product designed, built, deployed and EXPLAINED by @iusztinpaul  We highly recommend you follow his 7-part course, and build the system yourself. It is time to get your hands dirty, and learn tons of stuff ↓ my.mtr.cool/wzpktlaegw