Featureform

⌛ What does "real-time" really mean in the context of machine learning? 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. 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. 👉 Check it out: https://buff.ly/4ayIE4P

🚂 Thinking of hopping on the AI hype train? Before you do, listen to our latest podcast, where investor Gautam Krishnamurthi of GreatPoint Ventures (and a Featureform investor) chats with Featureform's 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 large language models (LLMs), venture valuations, and the impact of rising interest rates on the public markets. Lastly, you’ll learn about the transformative power of AI, how it's reshaping industries, and what investors seek in the next wave of groundbreaking companies. 🎧 Tune in to the full podcast here 👉 https://buff.ly/3RVX9ZL

👯♀️ Meet the perfect duo for data orchestration. Join us on Tuesday, July 23rd at 8 A.M. PT for "Building an Enterprise-Scale Feature Store with Featureform and Databricks," featuring our founder, Simba Khadder! In this one-hour session, you'll learn how Featureform simplifies defining, managing, and deploying ML features in a Databricks environment. We'll guide you through configuring Featureform with Databricks to orchestrate Spark transformations, show you how Featureform's versioning and lineage work seamlessly with Unity Catalog, and explore real-world examples of enterprises using Featureform and Databricks when working with petabytes of data across multiple sources. Toward the end of the session, there will also be time for Q&A. 🌟 We hope to see you there!

🔒 Unlock the power of your data! Join us for an insightful webinar as we delve into the intricacies of Retrieval-Augmented Generation (RAG) systems. We will explore how integrating unstructured and structured data using Featureform can enhance the capabilities of large language models (LLMs). 🔑 Key topics we'll cover: 🔹 The fundamentals of RAG and its applications in LLMs. 🔹 Techniques for integrating structured and unstructured data using Featureform. 🔹 Real-world case studies showcasing the benefits of this integration. Best practices and tips for optimizing your data workflows. Toward the end of the session, there will also be time for Q&A.

👀 Come check us out! 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! 😄 We hope to see you there!

🚂 Should all MLOps companies jump on the LLM hype train? Join Featureform's CEO Simba Khadder and Union.ai's founder Ketan U. as they tackle this topic and more on our latest episode (now with video!) of the MLOps Weekly Podcast! Episode Highlights: 🔸 Ketan shares his journey, starting with engineering roles at Lyft, Oracle, and Amazon, leading to the creation of Flyte. 🔸 The origins of Flyte at Lyft as a solution to operational challenges in deploying machine learning models 🔸 Ketan explains why traditional orchestrators like Airflow fall short for AI workflows and the innovative solutions Flyte provides. 🔸 Insightful discussion on the evolving landscape of AI, the role of LLMs, and the ongoing need for traditional machine learning models. 📺 Watch the full podcast here! https://buff.ly/3xgXBKV

😵 With the ever-growing market of feature stores, understanding how they can serve your MLOps workflow can be overwhelming. 💡 Join our one-hour webinar on Tuesday, May 28th at 8 A.M. PT, where we break it down for you! What We'll Cover: 🔸 A breakdown of various feature store architectures and their pros and cons. 🔸 Example configurations for each type and their implementation costs. 🔸 How they can be leveraged in a real-world MLOps workflow. Toward the end of the session, there will also be time for Q&A. 😄 We hope to see you there!

⭐ We've Rescheduled! ⭐ Join us on Tuesday, May 14th, at 8 A.M. PT, when our CEO and founder, Simba Khadder, will demonstrate using MLOps tools to build and deploy a recommender system. For use cases like recommender systems, feature engineering and data pipelines are critical to their performance. These use cases also often have to grapple with problems such as low latency serving, embeddings, and streaming data. Feature stores are a core solution a wide range of these problems. They streamline machine learning workflows for data scientists by unifying feature engineering processes. They provide consistent and reusable features across various models. Towards the end of the session, there will also be time for Q&A.

⭐ We've Rescheduled! ⭐ Join us on Tuesday, May 14th, at 8 A.M. PT, when our CEO and founder, Simba Khadder, will demonstrate using MLOps tools to build and deploy a recommender system. For use cases like recommender systems, feature engineering and data pipelines are critical to their performance. These use cases also often have to grapple with problems such as low latency serving, embeddings, and streaming data. Feature stores are a core solution a wide range of these problems. They streamline machine learning workflows for data scientists by unifying feature engineering processes. They provide consistent and reusable features across various models. Towards the end of the session, there will also be time for Q&A.

👀 Tune into our workshop next week to see Featureform in action with this real-world use case! Join us on Tuesday, May 7th, at 8 A.M. PT, when our CEO and founder, Simba Khadder, will demonstrate using MLOps tools to build and deploy a recommender system. For use cases like recommender systems, feature engineering and data pipelines are critical to their performance. These use cases also often have to grapple with problems such as low latency serving, embeddings, and streaming data. Feature stores are a core solution a wide range of these problems. They streamline machine learning workflows for data scientists by unifying feature engineering processes. They provide consistent and reusable features across various models. Towards the end of the session, there will also be time for Q&A.