Operational & Real-Time ML
Video: How to Make the Jump from Batch to Real-Time Machine Learning
If you’re a data scientist or ML engineer, the conversation about making that jump to real-time ML might come up sooner than you expect. Join Claypot AI CEO Chip Huyen and Tecton CTO Kevin Stumpf as they discuss how companies can make the jump from batch to real-time ML as easy as possible.
Challenges of Feature Monitoring for Real-Time Machine Learning
To be successful with machine learning, you need to do more than just monitor your models at prediction time. You also need to monitor your features and prevent a “garbage in, garbage out” situation. However, it’s extremely hard to detect …
HelloFresh Adopts Tecton as a Foundational Component of its MLOps Platform
"At HelloFresh, our philosophy is to avoid reinventing the wheel. When it came to feature stores, building one in-house was never a viable option. We needed to move quickly, and we wanted the best solution available. That’s what led us to Tecton."
Tide Safely Process Transactions Using Real-Time Fraud Detection
Tide thrives on making data-driven decisions to help its customers save time and money. To support this objective, a dedicated team of data scientists and engineers develop and productionize machine learning (ML) models that power automated experiences across Tide’s customer ecosystem. Tide aspires to automate as much of its business decision-making as possible with real-time ML. By adopting Tecton’s feature platform for ML, Tide now builds and delivers real-time predictive products in record time.