Welcome to the Tecton blog
Our thoughts on extracting ML signal from the data noise
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
Tide Deploys Models 2x Faster with Tecton
50% improvement Time to deploy new models to production decreased to just 1 month from 2 – 4 months. 9 months Engineering time recovered from continuing to build internal feature store. 3 engineers Resources repurposed from maintaining internal feature store. … Read More
Atlassian Deploys New Features in 1 Day with Tecton
200k Customer interactions improved per day 1 Day Time to build and deploy new features 2-3 Engineers Resources repurposed from maintaining internal feature store Atlassian builds tools that help teams collaborate and create together, including Jira Software and Confluence. To … Read More
Amazon SageMaker and Tecton: How to Choose the Right Feature Store on AWS
Amazon recently introduced SageMaker Feature Store, recognizing that one of the largest challenges facing data science teams is building and serving high-quality machine learning features both in training and in production. The team at Tecton could not agree more with … Read More
Feature Store in General Availability & Series B Funding
Today marks a big milestone for Tecton. We’re excited to announce that the Tecton feature store is now in General Availability, and that we have raised $35 Million in Series B funding from our existing lead investors, Andreessen Horowitz and … Read More
Why Tecton is Backing the Feast Open Source Feature Store
Today, we’re excited to announce that Tecton is becoming a core contributor to the Feast open source project, and that Willem Pienaar, creator of Feast, is joining the Tecton team. … Read More
Back to the Future: Solving the time-travel problem in machine learning
In Back to the Future II, Marty McFly gets an idea to purchase a sports almanac in the future and bring it back to the past to use for betting. Doc Brown warns him not to profit from time travel … Read More
Why We Need DevOps for ML Data
Getting machine learning (ML) into production is hard. In fact, it’s possibly an order of magnitude harder than getting traditional software deployed. As a result, most ML projects never see the light of production-day and many organizations simply give up … Read More