Fennel

Unlock your data science team’s potential with Fennel! Reduce reliance on engineering resources for feature engineering pipelines and increase iteration speed. Discover how industry leaders are leveraging Fennel’s cutting-edge platform to manage ⏱️real-time and batch ML features efficiently. Join co-founder and CEO Nikhil Garg for a live #demo on Tuesday, August 6th at 9 AM PST. Learn about: ✅  Achieving full data science team autonomy ⏱️ Real-time computation without Spark or Flink 💲 Cost savings with Fennel’s one-time compute model Register now to see how Fennel is transforming #ML infrastructure teams!🚀 #MachineLearning #FeatureStores #DataScience #AI #TechInnovation #MLInfrastructure #FennelAI  https://lnkd.in/enDG3NvX

ML 101: Handling Outliers in Machine Learning 👇👇 Outliers can significantly impact your ML models, leading to inaccurate predictions and lower performance. These extreme data points can mislead models or cause them to ignore valuable features. Here are steps to manage outliers effectively: ✔ Conduct exploratory data analysis to detect and remove outliers during training. ✔ Replace outlier values with “unknown” at inference time. ✔ Implement continuous monitoring to track outliers. Modern feature stores like Fennel offers built-in solutions for this. Interested in optimizing your models? Contact us for a demo! 😎 https://fennel.ai/ #fennel #machinelearning #ML #featurestore #featureengineering

Feature Stores, LLMs 🚀👇 With all the new ML feature stores launching, we at Fennel love the innovation the competition brings! It shows how quickly #ML feature stores are quietly and effectively gaining industry adoption. Feature stores are getting all the hype now, becoming essential and almost commonplace compared to the flashy new LLMs. Exciting times ahead for AI/ML infrastructure! If you're interested in feature stores, check out Fennel and contact us for a demo—we're the slickest feature store in the market! 😎 https://lnkd.in/d2Aq8aHn #MachineLearning #FeatureStores #AI #DataScience #Innovation #MLInfrastructure #LLMs #AIRevolution

🚀 Unlock Your Data Science Team’s Potential with Fennel! 🚀 Are you looking to increase the iteration speed of your data science team by reducing dependence on critical engineering resources for feature engineering pipelines? Are you seeking a platform to author, compute, store, manage, monitor, and govern both real-time and batch ML features efficiently? 🌟 Discover Fennel, a cutting-edge feature engineering platform. Join Fennel co-founder and CEO Nikhil Garg for our live demo and technical deep dive of Fennel’s feature engineering platform on Tuesday, August 6th at 9 AM PST to learn: ☑ How Fennel empowers data science teams to achieve full autonomy, reducing dependence on engineering support. ☑ Our approach to real-time computation without the need for Spark or Flink. ☑ The significant cost savings enabled by Fennel's one-time compute model. 📅 Register now to explore how Fennel is revolutionizing ML infrastructure teams! See you there! 😎 https://lnkd.in/enDG3NvX #MachineLearning #FeatureStores #DataScience #AI #MLInfrastructure #featureEngineering #FennelAI

🚀 Demystifying the 3 Generations of ML Feature Stores 🚀 ✅ 1st gen feature stores, were simple data repositories with external feature computation, lacking end-to-end visibility and quality checks. ✅ 2nd gen stores, integrated computation using Spark/Flink, but were complex and engineer-focused. ✅ 3rd gen system: enter Fennel💡 offering a Python-centric experience for data scientists, hiding complexity in the compute engine itself. Fennel's unified batch/streaming compute layer in Rust exemplifies this innovation. Are you ready to transform your ML workflows? Reach out to Fennel for a demo! #MachineLearning #FeatureStores #AI #DataScience #MLWorkflows #TechTransformation #Fennelai https://fennel.ai/

🚀 Demystifying the 3 Generations of ML Feature Stores 🚀 ✅ 1st gen feature stores, were simple data repositories with external feature computation, lacking end-to-end visibility and quality checks. ✅ 2nd gen stores, integrated computation using Spark/Flink, but were complex and engineer-focused. ✅ 3rd gen system: enter Fennel💡 offering a Python-centric experience for data scientists, hiding complexity in the compute engine itself. Fennel's unified batch/streaming compute layer in Rust exemplifies this innovation. Are you ready to transform your ML workflows? Reach out to Fennel for a demo! #MachineLearning #FeatureStores #AI #DataScience #MLWorkflows #TechTransformation #Fennelai https://fennel.ai/