Operational & Real-Time ML

apply() Spring ’24

Dive into practical workshops and technical sessions to gain best practices for transitioning from experimental models to highly scalable applications. Learn from the best engineers shaping the future of applied artificial intelligence and machine …

VideoOperational & Real-Time ML

Webinar: Supercharging Recommendation Systems With Generative AI & Real-Time Context

Join Tecton Travel (our fictitious travel company) as it builds an enhanced recommendation system with Generative AI to increase bookings on its website.

VideoOperational & Real-Time MLRecommender Systems

Batch, Streaming & Real-Time Data: How to Leverage Their Full Power for Production ML

Today, the data flows for ML applications are more complex and varied than ever. To gain a competitive edge, businesses are increasingly including online models in their products or real-time processes, introducing a new range of operational concerns—and often, an explosion in the number of data sources and transformations used by their ML models.

WhitepaperOperational & Real-Time ML

apply(ops)

This series focuses on platforms and architectures for production machine learning projects. Practitioners share best practices and lessons learned when designing machine learning systems.

VideoOperational & 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

Tempo

Tempo Leverages Tecton’s Feature Platform to Power Its Time-Tracking Solution With AI For Timesheets by Tempo, we’re trying to ingest events, featurize them, score them with our models, and get those predictions to our customers in a few …

Customer StoriesOperational & Real-Time ML

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