Machine Learning: The Past, Present, and Future

September 14, 2022 by

In this post, we take a look at the early days of getting ML into production, where we are today, and some predictions of what it will be like to build ML applications in the future.

Thought Leadership

Why Building Real-Time Data Pipelines Is So Hard

August 16, 2022 by

The hardest part of real-time machine learning is building real-time data pipelines. Learn how you can avoid common challenges in this post.

Operational & Real-Time Machine LearningThought Leadership

Managing the Flywheel of Machine Learning Data

July 28, 2022 by

Learn how achieving the flywheel effect with ML can help you and your team quickly iterate on models, creating a compounding effect that results in high performance and reliability.

Thought LeadershipOperational & Real-Time Machine Learning

What Is Operational Machine Learning?

May 26, 2022 by

In this post, Kevin Stumpf, CTO of Tecton, describes what operational ML really is and gives practical examples to understand how it works.

Operational & Real-Time Machine LearningThought Leadership

Why Centralized Machine Learning Teams Fail

May 16, 2022 by

How should you organize an ML team? Do centralized data teams work? In this article, David Hershey, solutions architect at Tecton, describes a common pattern we've seen across hundreds of companies using machine learning: centralized data teams …

Thought Leadership

Why Feature Stores Should Extend, Not Replace, Existing Data Infrastructure

May 11, 2022 by

During apply(meetup), Ben Wilson, from Databricks, gave a lightning talk on how ML projects shouldn't be built in isolation. At Tecton, we believe that great ML infra should integrate deeply with existing data infrastructure while providing …

Thought LeadershipFeature Stores

Building a Feature Store

January 20, 2022 by

As more and more teams seek to institutionalize machine learning, we’ve seen a huge rise in ML Platform Teams who are responsible for building or buying the tools needed to enable practitioners to efficiently build production ML systems. Almost …

Feature StoresThought Leadership

2021 at Tecton: A Year in Review

December 29, 2021 by

2021 is a wrap, and what a year it has been! As we head into the New Year, I want to take a look back at what we’ve accomplished this year and share with you some of the things I expect for 2022. Tecton has experienced a huge amount of growth this …

Company CultureThought Leadership

Journey to Real-Time Machine Learning

August 9, 2021 by

We speak with hundreds of enterprise machine learning teams every year. And we’ve observed a typical pattern for the adoption of machine learning in the enterprise. Not every organization follows the exact same steps, but we’ve seen the pattern …

Operational & Real-Time Machine LearningThought Leadership

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

March 17, 2021 by

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.  We also knew …

Thought Leadership

Back to the Future: Solving the time-travel problem in machine learning

July 6, 2020 by

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 because information from the future could create …

Thought LeadershipOperational & Real-Time Machine Learning

Why We Need DevOps for ML Data

April 28, 2020 by

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 …

Thought Leadership