Efficient & Accurate Data Generation for ML Models

October 17, 2023

Every effective ML models requires well-structured training data. This post covers the main challenges of constructing queries for feature retrieval and how the Tecton engineering team resolved them.

What is online / offline skew in machine learning?

April 5, 2023

The online/offline skew (aka the training/serving skew) is one of the biggest challenges in machine learning. Learn how you can reduce skew in this post.

Why You Need a Feature Platform for Data Product Iteration

March 22, 2023

Iteration is a crucial part of any product development lifecycle and can be very complex when it comes to data products. See how a good feature platform can help.

The Importance of Canary Testing to Ensure Feature Correctness

August 23, 2022

Learn how Tecton’s canary process was designed to keep Tecton’s feature platform reliable, stable, and scalable.

Scaling Tecton’s Data Engineering Team

February 2, 2022

2021 was a big year for Tecton! Our mission is to bring ML intelligence to every production application, which starts with the world’s best enterprise feature store to power machine learning. We made huge strides towards this mission:​​ …

Maintaining Feature Pipelines With Automated Resolution of Compute Failures

December 9, 2021

Customers rely on Tecton for providing uninterrupted access to their machine learning features. Tecton orchestrates and manages the data pipelines that compute these features, insulating the end user from their complexity. However, the pipelines run …

Tecton’s Security & Compliance Journey to SOC 2 Type 2

October 26, 2021

Learn more about Tecton's journey to gaining customer and user trust, from the early days to receiving our SOC 2 Type 2 report.

My observations since joining Tecton as VP of Engineering

October 12, 2021

I joined Tecton 4 months ago and would like to share my impressions while they are still fresh! I have spent almost 2 decades in various startups. In seven of them I was Director of Engineering and later VPE in quickly growing startups such as …

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

June 2, 2021

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 …

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

June 2, 2021

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 …

How Machine Learning Teams Share and Reuse Features

March 29, 2021

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 …