Mike Del Balso is the co-founder and CEO of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform, which enabled Uber to scale from zero to tens of thousands of ML models in production in just a few years.
Mike joined me on TFiR Insights to explain what is Operational ML and how different it is from ‘regular’ ML. It was a very interesting discussion in which we covered these topics:
- What exactly is Tecton all about, what problem was Mike trying to solve that led to the creation of his company?
- What exactly is operation ML.
- How different is operational ML from, if we may say, regular ML?
- Mike shared some use-cases of operational ML.
- Mike recollected the origin story of Michelangelo which he led as a project at Uber to help them leverage machine learning technologies back in 2015 when ML was not that mature to be used by companies like Uber in production.
- Mike talked about Challenges for operational ML, especially from the standpoint of data as it deals with a supermassive amount of data.
- Finally, we talked about his company Tecton and what kind of solutions and services it is offering.
- We also dug deeper into the company as it’s offering a SaaS solution. I wanted to know what kind of infrastructure Tecton is running on to ensure uptime and handle the demand that comes from users.
[su_note note_color=”#e4e4e4″ text_color=”#000″ class=”hvr-grow”]Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready feature store to make world-class machine learning accessible to every company. [/su_note]