New machine learning governance capabilities for Amazon SageMaker were made possible by Sai Latha Rudrabhatla
Bringing AWS features and services to life takes a big team of innovators, and Sai Latha Rudrabhatla is one builder who believes naysayers provide clarity.
Sai Latha Rudrabhatla joined the Amazon SageMaker team as principal product manager in July 2021. Since then, she has made it her personal mission to find and remove customer pain points when it comes to machine learning (ML) governance.
After many months of “working backwards with customers and field representatives to understand why they need what they need,” Rudrabhatla and her team of nearly two dozen have made their customers’ ML journeys smoother and more efficient by building Amazon SageMaker’s new ML governance capabilities.
Launched in November at re:Invent, these new governance capabilities for Amazon SageMaker will provide visibility into model performance throughout the ML lifecycle.
Get to know Sai Latha Rudrabhatla, and learn more about her innovation journey:
Describe your innovation journey in three words:
Chaos, focus, and teamwork. When we start any invention cycle, the degree of ambiguity is very high – to the extent that it just feels chaotic. There will be many customer problems to solve and various directions to follow, but I have to think about what are the most pressing items to tackle and what is the low-hanging fruit. Then as a product manager, I need to be laser-focused on solving that one problem out of many. Finally, delivering results requires teamwork, and I don’t want to only seek out people who think alike. I want people who will put holes into the proposal. Naysayers help provide more clarity, and that helps make the product better for the customer.
What was the most rewarding part of developing these capabilities and getting them to launch at re:Invent?
Personally, the most rewarding part is the collaboration with our customers. We’re developing a service that is solving customer pain. Up until now, everything was a just a proposal, in our minds. But once we launch, we will get real-time data and feedback from customers.
Second, I will always say is the learning experience you’re getting, especially with the people side of things. How do you handle stress (stress is essential for growth)? How do you talk to and align with different personality types? How do you get work done when everything is not under your control? What motivates people? Am I appreciating people with small wins along the way (even in the midst of a crisis)? These are all learnings I can put to use in my personal life and share with my sons.
If you could give one piece of advice to your past self just starting out on developing these capabilities, what would it be?
The piece of advice I would give myself is "don't take action or say anything in moments of stress. Take deep breaths, and take a break. Read a book. Go for a run. Do some stretches. Remember that this too will pass." Sometimes, when I’m stressed out, I just need to shut off my laptop and do something unrelated that will help me calm down.
Managers support that. Organization leaders support that. They recognize it. It’s not like people are telling me to stay online every second of the working day. Nobody tells me that. It’s just an odd expectation I put on myself.
What impact do you hope your team’s work will have on the cloud computing community and customers?
As companies invest in their ML solutions, I would like to see more and more new customers come to use Amazon SageMaker. And I’d like to see those who are already using it expand their usage because they trust the system, and because they believe in Amazon SageMaker.
What’s the last food you had delivered?
Pizza!
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This story is a part of the “Made Possible by” series, which gives you a glimpse into the builders behind the innovative technologies.
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