Jorge Torres, Co-founder & CEO of MindsDB – Interview Series

Jorge Torres, is the Co-founder & CEO of MindsDB, a platform that helps anyone use the power of machine learning to ask predictive questions of their data and receive accurate answers from it. MindsDB is also a graduate of YCombinator’s recent Winter 2020 batch and was recently recognized as one of America’s most promising AI companies by Forbes.

What initially attracted you to machine learning?

It’s an interesting story. In 2008, I was living and working in Berkeley for a startup called Couchsurfing and I saw this class, (cs188- Introduction to AI). Though I was not affiliated with the university at the time, I asked the prof. John DeNero if I could sit in for a class and he allowed me to. This professor was brilliant, and he really made everyone fall in love with the topic. It was the best thing that happened to me. I was amazed that computers could learn to solve a problem, I realized this was moving fast and decided to make it my career.

There are a few generational defining events in technology that only come around a few times in one’s lifetime. I was fortunate enough to be witness to the birth of the Internet but was far too young to be anything but a passive observer. I believe Machine Learning to be that next generational event, and I wanted to be a part of it in some meaningful way to drive forward the technology and the way we use it.

MindsDB started at UC Berkeley in 2018, could you share some insight from these early days?

UC Berkeley is one of the world’s great research institutions and has a history of creating and supporting open-source software, and we thought there was no better place to start MindsDB. Our values were aligned, they offered us our first check through the UC Berkeley Skydeck Accelerator and the rest they say is History.

The early days were not unlike many startups in the Bay region – Three people working long hours on something they all believed in, but had only a small chance of success. The only difference is rather than working in a dusty garage in Palo Alto we were in the relative comfort in the Skydeck Penthouse co-working space (rent free).

I believe that there is enormous power in data. The more a company has, the more they’re able to propel their businesses forward. But only if they’re able to get meaningful insights from it.

In the fall of 2017, my best friend Adam Carrigan (COO) and I came to the conclusion that too many businesses faced limitations when it came to extracting meaningful information from their data. They realized that one of the biggest limitations was in how many of these businesses were severely underutilizing the power of artificial intelligence. We believed that machine learning could make data, and the intelligence it can provide, accessible to everyone. That’s why we designed a platform that would allow anyone to use the power of machine learning to ask predictive questions of their data and receive accurate answers from it.

We call this platform MindsDB and are focused on continuing to make it incredibly easy for developers to rapidly create the next wave of AI-centered applications that will transform the way we live and work and for businesses to extract information from their data.

Why did MindsDB focus on solving the problem of being data centric as opposed to machine learning centric?

If you look at the vast majority of research in AI, a large percentage comes from academic institutions. ML has historically been model-centric because this is where research institutions can add perceived value; more research improves models or creates new ones thus producing better results. Being data-centric, on the other hand, adding better quality/more relevant data to an existing approach is not easily publishable (the key KPI for researchers).

However, the vast majority of applied machine learning problems today benefit far more from improved data than from improved models. This also aligns well with our mission to democratize machine learning, the vast majority of people outside of the Ml space don’t know very much about ML, but they sure do know a lot about their data.

We saw that there were two types of companies, on the one hand companies with data in the database, on the other, companies with that had not figured out databases yet, we realized that if a company was on the group of databases, their data maturity had already put them on the right track to be able to really apply machine learning, whereas companies that had not discovered databases yet, had a long way to go still, so we focused on providing value for those that could actually extract it.

How does MindsDB approach modeling and deployment in plain SQL?

We create representations of models as tables that can be queried, so effectively we remove the concept of ‘deployment’ out of the picture. When you type on a database CREATE VIEW that view is live right when the command is done processing, same thing when you do CREATE MODEL in mindsdb.

People love MindsDB due to the simplification you’ve brought to the ML-Ops lifecycle, why is simplifying machine learning deployment so important?

People love it because it abstracts unnecessary ETL pipelines, so less things to maintain. Our focus is to get users to extract the value of machine learning, by not thinking of maintaining the ML infrastructure if they already maintain data infrastructure.

What are some of the advantages and risks of being an open-source start-up versus a traditional start-up?

An Open Source project can start with just an idea, and people will help you build it along the way, on the close source approach you have to start with the same assumptions but you better be right because no one is going to help you improve your product (at least not in the same volume as in open source), think of open source as a collaborative product user fit approach.

MindsDB recently raised a $16.5M Series A investment from Benchmark, why is Benchmark the perfect investor fit and how does their vision match yours?

Benchmark has an impeccable record in our industry, Chetan has helped companies like mongodb, elastic, airbyte become the world leaders in their realms. We believe there is no better fit for MindsDB than Chetan and Benchmark capital.

Thank you for the great interview, readers who wish to learn more should visit MindsDB.