Tecton brings DevOps to machine learning Posted on : Jun 22 - 2020

A startup recently emerging from stealth is aiming to automate much of the process for feature development with the goal of making data scientists more self-reliant.

Models require algorithms and data, and when it comes to data, machine learning models have especially ravenous appetites. Yet, when it comes to transforming data into features consumed by the model, data scientists often have to reinvent the wheel as feature generation lacks the configuration management and automation processes associated with model workflows.

Tecton, a startup that recently emerged from stealth, is introducing a cloud-based service intended to address this bottleneck. They are striving, for feature generation, to introduce the same type of maintainability and reusability that Informatica did when it introduced automated data transformations to the data warehousing world back at its founding.

The cofounders of the company came from Uber, where they were responsible for Project Michelangelo, the company's machine learning platform.

At Uber, Michelangelo took care of the full lifecycle of ML from managing data to training, evaluating, and deploying models, followed by making and monitoring the predictions. Among the lessons learned from developing the Michelangelo platform was that feature generation lacked the degree of rigorous lifecycle automation that model development. All too often, data scientists had to keep reinventing the wheel when generating features, and the beginnings of that automation were built into the platform.

Those are the lessons that company founders took to creating Tecton. It addresses part of the problem impacting data scientists: they are often spending too much of their time performing data engineering. A couple years ago, in a survey jointly sponsored by Ovum (now Omdia) and Dataiku, we found that even in the best of circumstances, data scientists will still likely spend up to half their time wrestling with data.

In a company blog, Tecton lays out the problem in exhaustive detail. Many cloud AutoML services can automate aspects of algorithm development and feature selection, along with the workflow of moving models from training to deployment and production, but they lack any such automation with the feature engineering that feeds the model with data. View More