Machine Learning (ML) feature startup, Tecton, which has developed a cloud platform with functionalities of a matured enterprise to enable companies to create artificial intelligence projects in a jiffy, has raised $100 million in its Series C funding round.
The Series C funding round was led by Kleiner Perkins (adding to $60M raised in past rounds) bringing the total to $160 million. Snowflake and Databricks became strategic investors, and Tiger Global and Bain Capital Ventures joined as new investors, noted Mike Del Balso, Co-founder and CEO of Tecton, in a blog post.
What a day! Earlier today, we announced our $100M Series C funding round led by @kleinerperkins, Tiger Global, & @BainCapVC w/ participation from @SnowflakeDB @databricks @a16z & @sequoia. Read more from Tecton CEO @mikedelbalso here: https://t.co/chSnGtKKgi pic.twitter.com/qt5QKuCi4D
— Tecton (@TectonAI) July 12, 2022
Tecton, a startup that became an ML feature platform from a feature store, was founded by the developers of Uber’s Michelangelo platform. In machine learning, features are the data points that a neural network consults to make decisions.
The startup, which strives to make world-class ML accessible to various companies, tripled its annual recurring revenue (ARR) from the 2021 fiscal year. Its ARR growth rate accelerated more than 180 percent in the last quarter ending April 2022, as its customer base increased five times over the previous year.
“We believe any company should be able to develop reliable operational ML applications and easily adopt real-time capabilities no matter the use case at hand or the engineering resources on staff,” said Mike Del Balso.
“This new funding will help us further build and strengthen both Tecton’s feature platform for ML and the Feast open-source feature store, enabling organizations of all sizes to build and deploy automated ML into live, customer-facing applications and business processes quickly and at scale,” added Balso.
Tecton aims to ease putting Machine Learning into production
The startup will use the newly announced $100 million funding round to accelerate go-to-market initiatives and product development to fulfill its mission of making world-class ML accessible to every company. Currently, the feature platform for ML enables data scientists to turn raw data into production-ready features, the predictive signals that feed ML models.
“We expect the software we use today to be highly personalized and intelligent. While ML makes this possible, it remains far from reality as the enabling infrastructure is prohibitively difficult to build for all but the most advanced companies,” said Bucky Moore, partner of Kleiner Perkins.
“Tecton makes this infrastructure accessible to any team, enabling them to build ML apps faster. As this continues to accelerate their growth trajectory, we are proud to partner with Mike, Kevin and the team to pioneer and lead this exciting new space,” added Moore.
The startup is currently focused on managing data for offline training and online inference. However, ML teams are still left with much data work when building and running operational Machine Learning. Tecton’s vision is to support all of these capabilities within Tecton’s platform and the operating ML dataflow model.
The company wants to make feature engineering and data operations for Machine Learning applications simpler, more reliable, and more achievable for ML teams worldwide.
“It’s just a matter of time until large and small organizations integrate real-time predictive applications into their everyday operations. Given the strength of their technology and team, we believe Tecton is well-positioned to be the catalyst that helps enterprises experience firsthand the significant uplift of leveraging real-time or streaming data in predictive data products,” said Aaref Hilaly, Partner Bain Capital Ventures.
