Applied AI - Innovation That Matters - Debanjan Saha

More Intelligent Tomorrow: a DataRobot Podcast

In this episode of More Intelligent Tomorrow, we hear from Debanjan Saha, the President and Chief Operating Officer for DataRobot. He sat down with host Ben Taylor to discuss how he got to where he is and what he thinks is in store for artificial intelligence and machine learning.

Ben starts off by asking Debanjan about the unique journey that brought him to the world of AI/ML.

While Debanjan had originally planned to be a professor, a summer job at IBM Research started him on a climb up the technology stack. From systems and networking at IBM and a couple of start-ups, to databases and data lakes work in Amazon, and analytics with Google, the next logical step was to take on AI/ML. That brought him to DataRobot.
He says IBM Research was filled with talented technologists, Amazon was all about executing growth at scale, and Google taught him about building innovative distributed systems. He learned that experience is a key skill, and it takes time.

"There's no compression algorithm for experience." - Andy Jassy

Every technology goes through a hype cycle, where it’s the “next big thing.” It was the internet in the 80s, the dotcom boom in the 90s, and big data at the start of the new century. This decade looks to belong to artificial intelligence and machine learning.

The good thing about excitement around innovative technology is that it can lead to more investments, and investments can compress development time. It’s part of the growth phase.

"We will use AI without knowing that we are using AI. It's probably going to be in pretty much every decision-making process that we have.”

For AI/ML to succeed, we need to set proper expectations. AI can help you get to better decision-making, but there must be a return on investment to make it worthwhile.

Another expectation to be set is that it’s not just about technology. It's also about process and company culture. Software alone isn’t going to solve your process problems or your culture problems.

It will take time and experimentation to see how AI/ML will work best for any given situation. And don’t forget that failure is part of the process. Fail fast, iterate quickly. Find what doesn't work, and move on from there.
In healthcare, Debanjan notes that AI/ML is augmenting what humans can do in symptom analysis, image processing, and remote diagnostics. And in the field of sustainability research, it’s being used to develop sustainable harvesting solutions.

Debanjan goes on to share with Ben some of the most memorable lessons he has learned working in technology.

First is that plans never work. But a close second is to be curious. Do that, and you'll end up in the right spot.

"I think what’s most important for a technologist is to be intellectually curious and continuously learning."

Ben wanted to know how Debanjan's experiences in sales and marketing have influenced his technical decision-making. His advice is to focus on customers and how you can delight them. Know the problem the customer is trying to solve. Also, think big. Don't think 10% bigger, instead think 10x bigger.
Finally, Ben asks what advice Debanjan would give to people starting out.

Imagine where the world will be five years from now and work toward that. Think Big. Take risks. Be curious and learn.

Listen to the full episode to hear Debanjan speak on:

• His tech journey
• What he thinks of the hype around artificial intelligence
• The keys to successful AI/ML integration
• His thoughts on the future for AI/ML
• Why he suggests getting advice from outside your circle