Preventing Model Decay: Tecton + Fiddler for ML Drift Detection

May 8, 2025

Build drift-aware ML systems with Tecton's feature engineering and offline/online serving plus Fiddler's feature and model drift analysis.

Feature PlatformsPartners & IntegrationsFeature Stores

The Power of Tecton for Batch Machine Learning

June 20, 2023

Explore how Tecton’s feature platform for machine learning supports batch, streaming, and real-time data to meet the unique requirements of ML use cases.

Feature StoresFeature Platforms

A Closer Look at the Latest Feature Engineering Workflow Improvements in Tecton 0.6

March 31, 2023

Tecton 0.6 makes it easier to iterate on feature engineering workflows by providing a number of capabilities that make it easier to develop, test, and deploy ML features.

Feature PlatformsFeature StoresProduct Updates

Notebook-Driven Development with Tecton 0.6: Combating Fraud and Optimizing Dynamic Pricing

March 20, 2023

Notebook-driven development is a flexible and efficient approach that enables data scientists, ML engineers, and data engineers to define features in Python notebooks while constructing ML models.

Feature PlatformsFraud DetectionFeature Stores

Tecton 0.6 Enables Data Teams to Improve Iteration Speed When Building Batch, Streaming & Real-Time Features

March 15, 2023

The 0.6 release aims to provide a seamless transition from notebook development to production, the first feature platform to offer this capability

Feature PlatformsFeature Stores

Choosing the Right Feature Store: Feast or Tecton?

December 22, 2022

See a side-by-side deep-dive comparison of Feast and Tecton.

Feature PlatformsFeature Stores

Feature Store vs. Feature Platform

October 19, 2022

In part 2 of this 3-part series, we outline the differences between a feature store and a feature platform, and how they can help you overcome common challenges when building real-time ML in production.

Feature StoresFeature Platforms

Real-Time Machine Learning Challenges

October 19, 2022

In Part 1 of this series, we do a deep dive into the most common challenges of building real-time ML in production and why they're so challenging. Future posts will talk about how companies solve these challenges.

Feature PlatformsFeature Stores

Why Feature Stores Should Extend, Not Replace, Existing Data Infrastructure

May 11, 2022

During apply(meetup), Ben Wilson, from Databricks, gave a lightning talk on how ML projects shouldn't be built in isolation. At Tecton, we believe that great ML infra should integrate deeply with existing data infrastructure while providing …

Thought LeadershipFeature Stores

Building a Feature Store

January 20, 2022

As more and more teams seek to institutionalize machine learning, we’ve seen a huge rise in ML Platform Teams who are responsible for building or buying the tools needed to enable practitioners to efficiently build production ML systems. Almost …

Feature StoresThought Leadership

Announcing Feast 0.10

April 15, 2021

Learn the differences between Tecton, a fully managed feature platform, and Feast, an open source feature store. Today, we’re announcing Feast 0.10, an important milestone towards our vision for a lightweight feature store.  Feast is an open …

Feature StoresAnnouncements

How to Build a Fraud Model with a Feature Store

April 5, 2021

Many companies with platforms that involve financial transactions are looking to bring them in-house to some degree as they can have more control over the user experience and usually save money on transaction fees over outsourced solutions. While the …

TutorialsFeature Stores