FeatureformML
@FeatureformML
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The Virtual Feature Store 🔮 Featureform allows data scientists to define, manage, and serve machine learning features across their organization.
SF
Joined February 2021
🌟 We had an awesome happy hour last week!. Big shoutout to the @modal_labs team, including @alecpow and @charles_irl, for co-sponsoring last week's happy hour with us. We had a blast with you guys! 🚀
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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:
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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.
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🚂 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.
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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!
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🚂 Should all MLOps companies jump on the LLM hype train?. Join Simba Khadder and 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!
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RT @ClickHouseDB: Feature engineering at scale is hard but not if you use ClickHouse and @FeatureformML. We train a fraud detection model,….
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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.
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Our latest blog post on “Breaking Down "Real-Time" Machine Learning Systems” explores these topics and more. It dissects real-time ML systems into their various components, such as online inference, low-latency serving, and stream processing.
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⌛ What does “real-time” really mean in the context of machine learning?.
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