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Credit: Glynis Condon

Four women from AWS who are making machine learning accessible to all developers

From scientific research to product management and marketing, women are spearheading AWS’s mission to put the power of machine learning into the hands of all developers.

From scientific research to product management and marketing, women are spearheading AWS’s mission to put the power of machine learning into the hands of all developers.

AWS DeepLens enables developers to get familiar with computer vision. AWS DeepRacer is a fully autonomous 1/18th scale race car that allows developers to get hands on with reinforcement learning. And AWS DeepComposer enables developers to learn generative AI. AWS’s mission to democratize machine learning resonates with all four women featured in this article, published on International Women's Day 2020. At some stage in their education or career, each of them moved from technical fields to pursue careers in artificial intelligence. Their journeys mirror that of the thousands of developers who are getting hands on with machine learning with the “Deep” family of products.

For all four women, early exposure during childhood sparked a lifelong love for science. However, equally critical was seeing how science can make an impact in people’s lives – a tenet that is also central to scientific innovation at Amazon.

Sahika Genc, senior applied scientist

Sahika Genc
Sahika Genc, Amazon principal applied scientist
Credit: Alexandra Tatarzyn

Sahika Genc is a senior applied scientist focusing on AWS DeepRacer. Genc’s parents laid the foundation for a career in science at a young age. “My father is an electrical engineer. He wanted to spend more time with the family, and decided to work from home on a regular basis. To do that effectively, he couldn’t have my sister and I running around the house. So he purchased a Commodore. To keep me occupied, he asked me to type commands from a textbook into the computer, and program in Basic.”

Genc’s mother, who is an elementary school teacher, helped foster her interest in math.

“After I completed first grade, I received about average grades in reading and writing. My mother thought I might be more interested in math. And she was right.”

Genc remembers being struck by the electrical units that her father made for cars.

“They reminded me how my father’s work made a positive impact in how we lead our lives.” Genc’s desire to build real-world systems led her to pursue a PhD in computer and control systems. She studied not only the systems that had mechanical components, but also the algorithms that gave them instructions on how to work. Genc focused on finite state machines, which are mathematical models of computation. Finite state machines are at the heart of hidden Markov models that are widely used in in reinforcement learning.

When she joined Amazon, Genc wrote the working backwards document on cloud robotics.Genc says that it’s especially important for young girls to get hands on with new technologies. “In the early days of computing, many of the computer scientists were women,” Genc says. “We can do for our daughters what my parents did for me. They made sure I got hands on with computers – this not only made me familiar with the technology, but it also made me more resilient. It taught me in a very real way, that when you’re trying to create something, you will fail nine times of ten. The important thing is not to give up.”

When you’re trying to create something, you will fail nine times of ten. The important thing is not to give up.
Sahika Genc, senior applied scientist

Ambika Pajjuri, product leader

Ambika Pajjuri
Ambika Pajjuri, product leader, AWS AI

Ambika Paijuri, a product leader for AWS’ Deep product line, notes that a recent Gartner Group report on cloud AI developer services suggests that by 2023, 40 percent of enterprise development teams will be using automated machine learning services to build models that add AI capabilities to their applications, up from 2 percent in 2019.

“That’s why all of us within AWS believe it’s imperative that we provide the tools, services and products that enable developers to get ready for this growth.”

Pajjuri’ s mother, a meteorologist, was an inspiration who made her think about clouds and why the sky was blue (she knew how to say cumulonimbus before she knew how to write). Pajjuri developed a love for engineering at an early age. However, she transitioned to a broader, product-focused role in the course of her career.

“I had completed my master’s with a focus on networking and telecommunications,” Pajjuri says. “However, I found myself getting interested in not just the engineering work, but also in working across the breadth of the customer experience.”

At mobile technology provider Airvana, Pajjuri helped develop the mobile data services that we take for granted in our cell phones. She also helped pioneer the development of femtocells and wireless access for cellphones – used even today to improve wireless coverage in our homes. At Amazon, Pajjuri has been a product leader on the Echo family of devices, where she’s led the development of several Echo devices (Echo Input, Echo Flex, Echo Auto), and Alexa’s multi-room music feature. Her work in the field of artificial intelligence further drove her interest in natural language processing – following which, a role with the AWS AI organization was a natural progression.

Pajjuri advises her team members to constantly meet with customers. Pajjuri’s counsel to her team comes from a very real place. After all, staying focused on the customer is central to her career journey. It led to her transition to a career in product management.

For Pajjuri, there is no substitute to hands-on experience. “When you’re a manager of a product team, it’s important to understand the technical aspects at a fundamental level,” Pajjuri says. “When I began, I took courses like Andrew Ng’s machine learning courses. Today, developers can also turn to AWS DeepLens, AWS DeepRacer and AWS DeepComposer!”

Jyothi Nookula, product owner

Jyothi Nookula, product owner, AWS AI

Nookula. a member of Pajjuri’s team, is a product management lead who owned the first product, AWS DeepLens, and worked most recently on AWS DeepComposer. In her roles, Nookula works with tech, hardware and business teams to understand customer needs, prioritize features, and make decisions on a wide variety of matters ranging from identifying chip providers to finalizing the user interface design.

Nookula remembers discussing X-rays and physiology at the dinner table – conversations that sparked a lifelong love for science and engineering.

After her education, Nookula’s work led her in a more unexpected direction. She found herself collaborating with industry-leading scientists to develop machine learning algorithms that could predict your real biological age based on your DNA (versus just your physical age). Nookula won an Innocentive worldwide competition for her work, in addition to attracting interest from leading corporations in the healthcare space. Nookula’s interest in machine learning intensified when she was a product manager for printing company 3D systems.

“We were using 3D printers to print the most unexpected objects from dentures to models of the human heart, and even chocolates,” Nookula says. “Machine learning has an important role to play in shaping the parts of 3D printed components.”

Nookula joined Amazon in 2016; the company’s customer-obsessed approach to science was critical to her decision to join the company. Today, as part of her role, Nookula has met with developers from a large number of enterprises.

Every Deep product is explicitly designed to get developers hands on with machine learning. This philosophy resonates with Nookula – as getting hands on has been instrumental to their career progression into artificial intelligence. Nookula gives the example of when she used to commute to the Seattle offices during the early days of her career at Amazon.

“I am the kind of person who likes to do things hands on. When I joined Amazon, I built a 3D self-driving car simulator using a Go Pro camera. I used to operate the car every day during my walk to work. My confidence grew with every accomplishment, major or otherwise. After a few months, I felt bold enough to reach out to Dr. Matt Wood about my interest and prior work in AI.”

Alexandra Bush, marketing lead

Alexandra Bush, marketing leader, AWS AI

Alexandra Bush leads product marketing for the AWS Deep Device family of products. She says that their group’s mission is an important and urgent one.

“The World Economic Forum estimates that artificial intelligence could create up to 58 million new jobs in the next few years,” says Bush. “And there were less than 300,000 skilled AI engineers at the end of 2018.”

Bush’s marketing team focuses on the developer experience, and the different ways they experience the product.

“We want to deliver an educational experience, but also make sure that we’re making it fun and engaging.”

Prior to Amazon, Bush worked at Intel for 13 years in their sales and marketing organization. At Intel, she moved from a business planning and operations role to partnering with customers on developing joint go-to-market strategies. In the course of her career at Intel, she worked with Amazon Alexa and AWS teams to develop joint marketing campaigns for products like the Echo.

“Even in those early days, it was exciting to see that artificial intelligence was a very real thing that’s going to have an impact on the lives of millions of people. I've always had an interest in technology and how technology can help businesses. It’s really fulfilling to develop campaigns like the AWS DeepRacer League with clear and compelling messaging that helps developers get hands-on with machine learning in fun and engaging ways.”

The results of AWS’ customer-obsessed approach are clear to see. Organizations like Morningstar have kicked off the first company-wide internal Amazon Web Services (AWS) DeepRacer competition.

AI can be complex, but at AWS, we’re making a concerted effort to help people follow their passions and interests. When you do that, you can help people realize their career goals across the world.
Alexandra Bush, marketing lead

“It’s great to see Morningstar is investing in machine learning to automate data-collection processes, enabling fresher, almost real-time data while freeing resources to work on newer data sets,” says Bush.

“AI can be complex, but at AWS, we’re making a concerted effort to help people follow their passions and interests. When you do that, you can help people realize their career goals across the world.”

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