Tecton

🚀 Scaling Real‑Time ML Infra at Coinbase — Virtual Talk  • Sub‑second freshness, <100 ms latency  • Batch + streaming with Databricks & Tecton  • Built-in feature pipeline observability + governance  • Sequence frameworks for deep learning use cases 📅 June 24, 10 AM PT (1 PM ET).   🔗 Register here: https://lnkd.in/gw9tAd4n #MLOps #RealTimeML #FeatureStore #FraudDetection

Your ML models are only as good as the data feeding them. Data drift silently degrades ML model performance in production. When feature distributions shift over time, even well-trained models become unreliable. To address this, we’ve created a new guide on building drift-aware ML systems using the Tecton + Arize AI integration. Why it matters: Models trained on historical data can break down when real-world inputs change — especially critical in high-stakes use cases like fraud detection." The solution: Detect drift before it impacts performance through comprehensive monitoring from feature engineering to production. Includes a complete fraud detection example showing how to set up consistent feature definitions, log training baselines, monitor drift, and get proactive alerts. Don't wait for model performance to degrade. Build drift detection into your ML infrastructure from day one. Read the full guide: https://lnkd.in/gwccctEc #MachineLearning #MLOps #DataDrift #FeatureStore #ModelMonitoring

“Not everything in AI is about LLMs.” In this conversation with MLOps community, our CEO Michael Del Balso dives deep into the real-world ML systems that drive actual business value—like fraud detection, real-time pricing, and recommendation engines. Hear about lessons from Uber, the origin of Tecton, and what it takes to get ML into production today. 🎧 Watch or listen here: https://lnkd.in/grTcuqGU

Focused on fraud, personalization, or infrastructure for modern ML systems? Learn how Joseph McAllister and the ML Platform team at Coinbase use Tecton to power real-time fraud detection and personalized recommendations at scale—contributing to tens of millions in business impact.  🗓️ June 24 | 10am PT https://lnkd.in/gw9tAd4n

Fraud moves fast. Your ML system should move faster. When your model waits too long for feature data, you risk missing the moment to block fraud. That’s why Tecton now supports Feature Retrieval Latency Budgets—a powerful new capability that lets you: ⚡ Set precise latency constraints per request ⚡ Skip slow features while still returning a prediction ⚡ Avoid pipeline failures caused by one lagging feature ⚡ Optimize compute costs by cutting wasteful queries Whether you’re powering real-time fraud detection, credit risk scoring, or personalization, milliseconds matter—and Tecton now gives you full control. Learn how it works (with code examples): https://lnkd.in/e3EjX6nv #frauddetection #machinelearning #mlops #realtimeML #fintech

New blog post from Kevin Stumpf: Transfer Learning – Lessons from Predictive ML for Agentic AI 👀 A decade ago, putting ML into production meant wrestling with brittle pipelines and unreliable infrastructure. That work gave rise to feature stores, observability tools, and model registries—tools built to support real-time decisions at scale. Now, we’re seeing similar patterns emerge in agentic systems. Underneath the shift in form factor, the data infrastructure needs remain familiar: low-latency access, high freshness, time travel, and governance. This post draws a clean line from the lessons of Predictive ML to what’s now needed for LLM-based agents. If you’re thinking about what it takes to scale beyond the prototype, give it a read: https://lnkd.in/gcv8jVy8 #mlops #realtimeml #agenticai #frauddetection #aiinfrastructure

Coinbase is raising the bar on what real-time ML can do. In their latest blog post, Joseph McAllister and Austin Rainwater from the Coinbase ML team share how they’ve built streaming sequence features using Tecton and Databricks—capturing rich user behaviors like login sessions and trading patterns in real time. These features feed into deep learning models like LSTMs and Transformers to detect fraud and deliver personalized recommendations, all within milliseconds. Even more impressive? These sequence features are now among the top contributors to model performance globally at Coinbase. Read more about their approach and the architecture behind it: 👉 https://lnkd.in/ekhShS-U #MachineLearning #RealTimeML #AI #FraudDetection #DataEngineering #MLOps #Tecton #Databricks #Coinbase

Don't Let Your ML Models Decay: Introducing the Tecton + Fiddler Integration Is your fraud detection model suddenly generating more false positives? Your customer churn predictions becoming less accurate? The culprit might be feature drift. We've partnered with Fiddler to solve this critical ML operations challenge: ✅ Tecton manages your feature pipelines, ensuring consistency between training and serving  ✅ Fiddler monitors 30+ ML metrics and detects drift in both model outputs and input features ✅ Together, they create a closed-loop system that catches issues before they impact your business In our latest integration guide, we walk through a fraud detection example that shows how to: ✅ Build a robust feature service in Tecton ✅ Register and baseline your model in Fiddler ✅ Implement real-time monitoring ✅ Set up alerts when features drift beyond acceptable thresholds Check out the complete integration guide to learn how you can implement this powerful ML monitoring solution in your own workflows! #MachineLearning #MLOps #FeatureStore #MLMonitoring #AIOpsX https://lnkd.in/g2tkcyc8

Tomorrow join over 500 registrants for a virtual talk on 🤖 AI-Assisted ML Engineering from our CTO, Kevin Stumpf.

Real-time decisions need real-time features. We’re excited to partner with Taktile to help risk and fraud teams move faster, with production-ready ML features and low-code decisioning that scale. By combining Tecton’s feature platform with Taktile’s logic engine, teams can: 🔄 Iterate on risk models in days, not months 🚀 Serve low-latency features from streaming data 🧩 Backtest full workflows with enriched historical context 🏗️ Ship real-time fraud and credit decisions with enterprise-grade control Together, we’re helping organizations modernize their risk stack—from data to decision. Read the full post: https://lnkd.in/eghmgWdu #mlops #frauddetection #realtimeml #fintech #features