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Welcome to the Tecton blog

Our thoughts on extracting ML signal from the data noise

FEATURED POST

Posted by  Mike Del Balso
October 20, 2020

What is a Feature Store?

Posts

Real-Time Aggregation Features for Machine Learning (Part 2)

In the following sections, we describe an approach to solving these challenges that we’ve proven out at scale at Tecton, and that has been used successfully in production at Airbnb and Uber for several years. The discussed approach explains Tecton’s implementation which relies entirely on open source technologies … Read More

Posted by  Kevin Stumpf
June 2, 2021

Real-Time Aggregation Features for Machine Learning (Part 1)

Machine Learning features are derived from an organization’s raw data and provide a signal to an ML model. A very common type of feature transformation is a rolling time window aggregation. For example, you may use the rolling 30-minute order count of a restaurant to predict the order preparation time of your favorite food delivery service … Read More

Posted by  Kevin Stumpf
June 2, 2021

apply() Conference Recap

Last month, on April 21 and 22, Tecton hosted apply(): the ML data engineering conference to share the latest best practices in ML data engineering. The conference brought together industry thought leaders and practitioners from over 30 leading organizations and … Read More

Posted by  Manmeet Gujral
May 25, 2021

Tecton Named Gartner Cool Vendor

We’re excited to announce that Tecton has been named a 2021 Gartner Cool Vendor in Enterprise AI Operationalization and Engineering(1). This is great recognition for Tecton’s vision of solving the data problem for ML by making feature stores accessible to … Read More

Posted by  Gaetan Castelein
May 18, 2021

Announcing Feast 0.10

Today, we’re announcing Feast 0.10, an important milestone towards our vision for a lightweight feature store.  Feast is an open source feature store that helps you serve features in production. It prevents feature leakage by building training datasets from your … Read More

Posted by  Willem Pienaar
April 15, 2021

How to Build a Fraud Model with a Feature Store

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 advantages … Read More

Posted by  Jack Wells
April 5, 2021

Announcing apply()’s Speaker Lineup

Tecton is hosting apply(): the ML Data Engineering conference on April 21 and 22, bringing together industry thought leaders and practitioners from over 30 organizations to share and discuss the current and future state of ML data engineering. This conference … Read More

Posted by  Mike Del Balso
March 31, 2021

How Machine Learning Teams Share and Reuse Features

What does an “ML-enabled” company look like? The companies that come to mind, like Uber, Twitter, or Google, have tens of thousands of machine learning (ML) models in production. They use these models to make intelligent predictions across the business … Read More

Posted by  Jay Parthasarthy
March 29, 2021

Data Software-as-a-Service: The Case for a Hybrid Deployment Architecture

Lior Gavish & Kevin Stumpf As founders of companies that build solutions designed to help teams deliver on the promise of data, we knew we wanted to build great products that are easy to deploy and manage for our customers.  … Read More

Posted by  Kevin Stumpf
March 17, 2021

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