Managing and transforming the data is the most challenging and costly step in putting the machine learning (ML) models into production. It’s challenging to keep up with, let alone manage, the enormous volumes of data being created. The need to reduce sources of bias, such as training-serving skew and data leakage, while merging real-time and historical data to quickly make judgments at scale based on the most recent information available is just one of the difficulties.
California-based Tecton is attempting to address this issue with an enterprise-ready feature platform that can deploy machine learning applications to production in minutes instead of months. With the ability to construct and automate feature pipelines that generate feature values from batch, streaming, or real-time data, the company provides a comprehensive cloud-based feature platform for ML. This platform combines the feature store features of storing, sharing, and reuse.
Jeremy Hermann and Kevin Stumpf, two former Uber coworkers, and Del Balso, who oversaw the machine learning teams for Search advertisements at Google, co-founded Tecton in 2019. The group had developed Michelangelo while they were still at Uber, an AI platform used by Uber internally to perform tasks like automating fraud detection and generating market forecasts. The success of Michelangelo served as inspiration for Tecton’s technology, which aims to give every business access to the features of an advanced enterprise feature store.
The company’s customer base has grown five times during the past year, and its ARR (annual recurring revenue) has nearly tripled. Tecton sees a time when deploying ML will be as simple as deploying code. They intend to simplify, increase reliability, and, quite frankly, increase the reachability of feature engineering and data operations for ML applications for ML teams everywhere.
In addition to the $60 million raised in earlier rounds, Tecton has recently raised $100 million in a Series C funding round led by Kleiner Perkins. Tiger Global and Bain Capital Ventures joined as new investors, and Snowflake and Databricks became strategic investors.
Tecton plans to use these funds to continue evolving and strengthening its feature platform and scale its engineering and go-to-market teams.
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Consultant Intern: Currently in her third year of B.Tech from Indian Institute of Technology(IIT), Goa. She is an ML enthusiast and has a keen interest in Data Science. She is a very good learner and tries to be well versed with the latest developments in Artificial Intelligence.