Tecton

Tecton was founded in 2018 by the creators of Uber’s Michelangelo platform, who envisioned a more scalable and efficient approach to managing machine learning (ML) data. Their mission has been to enable companies to deploy real-time ML models effortlessly, revolutionizing how ML is integrated into everyday business operations. Tecton is a leading feature platform that empowers data teams to build, deploy, and monitor production-grade ML features at scale. With its innovative tools, Tecton streamlines the ML lifecycle, helping businesses achieve faster and more reliable model outcomes. Ravi Trivedi & Mary Solbrig joined Elliot Kipling on Enginears to help us uncover more about Tecton along with their next 12 months of growth. Full podcast in the comments below ⬇️

"The model performed great in testing..." Sound familiar? Training an accurate model is one thing. Keeping it accurate in production? That's a whole other challenge 🎯 Our newest blog breaks down how ML models struggle in the real world, and why feature engineering can help your models stay sharp. Learn more:

🚀 Starting in less than an hour! Don’t miss our live webinar: "Closing the Gap from Data Science to Production AI": https://lnkd.in/g9ke5FgD We’ll explore how to break down silos between data science and engineering, streamline collaboration, and accelerate your path to production-ready AI. Can’t make it? No worries – sign up to get the recording and watch it on your own time. #AI #MachineLearning #DataScience #Webinar

⚒️ Are data engineering challenges slowing your AI projects? Join us Dec 4, at 9AM PST to learn how AI teams are solving the 'last mile' problem in AI - getting data reliably from prototype to production without months of custom engineering. We'll cover: • How to automate production-ready data pipelines for both traditional ML and generative AI • Techniques for ensuring consistency between training and serving data • Serving AI data with sub-second latency at scale Register here: https://lnkd.in/g9ke5FgD

Love seeing how HomeToGo is using Tecton to power personalized travel experiences in real-time! 🚀 Thank you for sharing your story with us HomeToGo

One of my favorite aspects of working at Tecton has been the freedom to explore opportunities to make an impact beyond my usual engineering projects. My first of such side-quests was spending a couple quarters collaborating directly with customers to implement their use cases in Tecton. Soon after embarking on this journey, I realized that no matter how flexible and powerful the product is, success depends on providing a simple, intuitive framework paired with clear documentation and guidance to help users establish the right mental models and feel confident in leveraging Tecton for their use cases. That leads to my latest side-quest of creating a series of video tutorials designed to offer a comprehensive guide to core feature engineering concepts and showcase how Tecton’s framework can be used to implement a wide range of data pipelines. The series covers foundational topics like batch, streaming, and real-time pipelines, as well as more nuanced subjects like stream compaction and materialization delays. The goal is to empower both new and experienced users to build a solid understanding of the fundamentals and confidently navigate the feature engineering landscape using Tecton. You can explore the video tutorials now at https://lnkd.in/eNm6H5HP. Happy learning!

How smart is your AI if it doesn’t understand your business or customers? 🧠 In our latest blog post, we dive into why context—timely, relevant data from every corner of your organization—is key to building smarter AI applications. Without fresh, business- and user-specific data, even the best models can struggle in production. Highlights include: 🔎 Why critical context, like recent transactions for fraud detection or up-to-date customer info for support, keeps AI accurate and reliable. ⚙ Why dynamic data infrastructure—integrating structured features, embeddings, and LLM prompts—is crucial for real-time, scalable AI. 📈 How Tecton helps teams convert raw data into actionable AI context, boosting model accuracy, deployment speed, and cost-efficiency. Read the full post 👉 https://lnkd.in/euRGeQJ5

Today marks Tecton's 6th anniversary! 🎉 What started as an idea born from our experience building Michelangelo at Uber has grown into something truly remarkable. In 2017, we saw firsthand how a feature platform could enable a Cambrian explosion of machine learning within an organization. That insight led us to found Tecton in 2018, with a mission to help every company successfully operationalize AI. We're incredibly proud of how far we've come: from pioneering the first enterprise feature platform in 2020, to powering mission-critical AI applications at some of the world's leading companies, to launching Tecton 1.0 this year with groundbreaking capabilities for both predictive ML and generative AI. But what truly energizes us is seeing how our platform helps teams every day to build exceptional AI products. Thank you to our amazing customers, partners, and the incredible Tecton team for being part of this journey. As AI becomes central to how businesses operate, our mission is clearer than ever: bridging the gap between data and AI systems to help organizations build reliable, production-ready AI. Here's to the next chapter! 💫 #AI #GenAI #MLOps #FeatureStore #FeaturePlatform

😱 Is it giving you nightmares to build a production LLM-powered app? You're not alone! Engineers are running into these scary scenarios: 👻 Costly hallucinations in customer-facing apps 🧟♀️ Complex manual work scaling your RAG system 💀 Generic, impersonal responses that don’t know the user 🧛 Inefficient prompts, overstuffed with irrelevant info Don't let your GenAI use case turn into a technical horror story. Our new blog has the practical engineering strategies to keep the monsters at bay! #LLM #GenAI #MLOps