All Resources

Discover how FanDuel built a powerful ML platform for personalized experiences

Join the webinar on July 11 at 9 am PT

EventCustomer StoriesFeature Platforms

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apply() is an ML engineering community conference where practitioners will share best practices and architectures of choice for building production machine learning systems. Join us to learn from and connect with your peers!

EventOperational & Real-Time ML

The Buyer’s Guide to Evaluating ML Feature Stores & Feature Platforms

If you’re looking for a feature store or feature platform for machine learning and don’t know where to start, this guide is for you! It will help you frame your evaluation to ensure you choose the best solution for your organization’s needs.

WhitepaperFeature PlatformsFeature Stores

Webinar: Notebook-Driven Development in Tecton 0.6

Join us for an in-depth look at Tecton's latest release, version 0.6, and the new features that are designed to streamline and simplify feature engineering workflows.

VideoFeature PlatformsProduct Updates

The ML Data Engineering Event for Recommender Systems

Watch the replay to hear from speakers at Slack, Cookpad, and ByteDance!

VideoOperational & Real-Time MLRecommender Systems

Feature Stores for Real-Time Machine Learning. Build or Buy?

Data sources, feature freshness, data pipelines, training / serving skew—learn how these come into play in the build vs. buy decision.

WhitepaperFeature PlatformsFeature StoresOperational & 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.

VideoOperational & Real-Time ML

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 …

PostOperational & Real-Time ML

Choosing the right feature store: Feast or Tecton?

High-level and hands-on comparison to help you choose the best feature store for your machine learning use cases

VideoFeature PlatformsFeature Stores

A Practical Guide to Building an Online Recommendation System

Building an online recommendation system? Our resident recsys expert offers advice to help you reduce costs, manage complexity, and more.

PostRecommender Systems

Real-Time Machine Learning for Financial Services

How Tide built real-time fraud detection and loan approval ML applications using Tecton’s feature platform

VideoCustomer StoriesFraud DetectionOperational & Real-Time ML

Feature Store vs. Feature Platform: Which One Is Right for Your Team?

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.

PostFeature PlatformsFeature Stores

Why Building Real-Time Data Pipelines Is So Hard

The hardest part of real-time machine learning is building real-time data pipelines. Learn how you can avoid common challenges in this post.

PostOperational & Real-Time ML

Scaling Real-Time ML at Cash App with Tecton

Hear Cash App’s journey through various generations of its core machine-learning capabilities and how Tecton’s real-time feature platform helps them deliver world-class recommendations.

VideoCustomer StoriesFraud DetectionOperational & Real-Time ML