The Virtual Feature Store 🔮 Featureform allows data scientists to define, manage, and serve machine learning features across their organization.
SF
Joined February 2021
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🌟 We had an awesome happy hour last week!
Big shoutout to the @modal_labs team, including @alecpowell and @charles_irl, for co-sponsoring last week's happy hour with us. We had a blast with you guys! 🚀
Learn about the evolution of feature stores with our founder, @simba Khadder, as he chats with Demetrios on the MLOps Community Podcast!
Click here to listen to the full podcast: buff.ly/4esiDXf
🚂 Thinking of hopping on the AI hype train?
Before you do, listen to our latest podcast, where Gautam Krishnamurthi of GreatPoint Ventures chats with Featureform CEO Simba Khadder about the rapidly evolving world of AI and its impacts on the future of venture capital investing.
They also discuss the latest trends in LLM's, venture valuations, and the impact of rising interest rates on the public markets. Lastly, you’ll learn about the transformative power of AI, and what investors seek in the next wave of groundbreaking companies.
Using Structured Data in RAG to Power LLM Applications xitter.cf/i/broadcasts/1YqKDggLQ…
Visit our booth at the Databricks Data + AI Summit this week (we're booth #6, near the pickleball court!), where you can learn more about our open source and enterprise products, get a live demo and, most importantly, get some awesome swag including a Feature Creature plushie!
🚂 Should all MLOps companies jump on the LLM hype train?
Join Simba Khadder and Union.ai's founder, Ketan Umare, as they tackle this topic and more on our latest episode of the MLOps Weekly Podcast!
📺 Watch the full podcast here! buff.ly/3xgXBKV
MLOps Feature Stores Explained xitter.cf/i/broadcasts/1OwxWYVlo…
MLOps Workshop: Build and Deploy Recommender Systems xitter.cf/i/broadcasts/1eaJbgAnW…
Workshop: Building an MLOps Platform on AWS xitter.cf/i/broadcasts/1OdKrjpeq…
Featureform 101: Getting Started with Featureform xitter.cf/i/broadcasts/1vAxRvMmz…
FeatureformML retweeted
Feature engineering at scale is hard but not if you use ClickHouse and @FeatureformML. We train a fraud detection model, using ClickHouse to transform and manipulate data at scale and FeatureForm to handle data versioning and model serving.
clickhou.se/3U1ssng
This article will help you navigate the complexities of deploying a real-time ML system. Whether you're architecting it from scratch or working on an existing system, demystifying the often-overloaded term "real-time machine learning" will help you build better platforms.
Continuous Delivery for Machine Learning with MLOps xitter.cf/i/broadcasts/1YpJkwnrb…
Getting RAG Systems Into Production with MLOps xitter.cf/i/broadcasts/1YpJkwdXz…
