All Resources

apply()

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

apply(risk): ML Data Engineering for Risk & Fraud Detection

Watch the replays from companies like Remitly, Block, Affirm, and more!

VideoFraud DetectionOperational & Real-Time ML

Webinar: Creating Better Machine Learning Features for Real-Time Decisions

A machine learning model can only be as good as the features that power it.  The challenge, however, is that ML models require high-quality data that can be difficult to process and serve in production.  Transforming data into features that …

VideoFeature PlatformsOperational & Real-Time ML

The State of Applied Machine Learning 2023

We surveyed over 1700 ML practitioners for this inaugural report on the state of applied machine learning. It provides a comprehensive overview of applied ML, and shares the challenges and opportunities in the space, along with common trends across …

WhitepaperOperational & Real-Time ML

Virtual Hands-On Lab: Building a Feature Pipeline

Through a series of code examples, we’ll walk through the concepts and code that will help you build a modern technical architecture that simplifies the process of managing production ML models and features.

VideoTutorials

Breaking Out of the Training Data Bottleneck: How to Generate High-Quality Data, Faster

In machine learning, it's known that you can’t create a great model without great training data. You need to train your ML models the right way, with the right data, in order to get accurate predictive outputs.

Whitepaper

Discover how FanDuel built a powerful ML platform for personalized experiences

Learn how FanDuel engineered and built a powerful ML Platform to deliver personalized experiences to their users.

VideoCustomer StoriesFeature Platforms

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

apply(recsys): 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