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My observations since joining Tecton as VP of Engineering
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 … Read More
Samantha Rydzewski
We are happy to announce a new blog series here at everyone’s favorite feature store – Featured Tectonauts . In this blog series, we will be highlighting the amazing characters we get to interact with daily here at Tecton. Want … Read More
Introducing Low-Latency Streaming Pipelines for Real-Time ML
Intro We’re excited to announce that Tecton now automates low-latency streaming pipelines, to help organizations quickly and reliably build Real-Time ML models. Real-Time ML means that predictions are generated online, at low latency, using real-time data; new events are reflected … Read More
Journey to Real-Time ML
Intro We speak with hundreds of ML enterprise ML teams every year. And we’ve observed a typical pattern for the adoption of ML in the enterprise. Not every organization follows the exact same steps, but we’ve seen the pattern validated … Read More
apply(meetup): Announcing the Speaker Lineup for August 11
Here we go again! It seems like just yesterday we were hosting the first iteration of apply(). Now back by popular demand, we’re hosting apply(meetup) on August 11th from 9am to 12pm Pacific Time … Read More
apply() Highlight: How to Use Fast & Fresh Features for Online Predictions
Online inference can significantly increase the impact of a machine learning model by incorporating fresh data. For example, if we’re building product recommendations for an e-commerce site, our model will benefit from information about what other products the user recently … Read More
apply() Highlight: How Feature Logging Enables Real-Time ML
Generating training data for an online, real-time machine learning system can be tricky. In order to guard against temporal data leakage, training events must use only historical features that were valid as of that point in time. This requires effectively … Read More
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
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
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