[HS Conversations] I’d like to tell women that the glass ceiling is not as hard as the floor is sticky: Vaishali Kasturae, AWS India
Vaishali Kasturae, India leader for Strategic Projects, AWS, speaks to HerStory on how AWS is building the machine learning (ML) ecosystem in India with the AWS DeepRacer Women’s League and why more women should be encouraged to work on emerging technologies.
Vaishali Kasturae’s career spanning 25 years has seen three interesting pivots so far. She started her banking and financial services career, moved to the knowledge process outsourcing industry, and two-and-a-half years ago, entered the world of technology with Amazon Web Services (AWS).
As India Leader for Strategic Projects at AWS, she is helping build the machine learning (ML) ecosystem in India and working towards a diverse workforce through initiatives like the AWS DeepRacer Women’s League – India 2021.
In a conversation with HerStory, Vaishali takes us through the League’s entry into India, the intense competition, and introducing young girls to AI. She also tells us why organisations need to play an essential role in nurturing talent and retaining them.
Edited excerpts from the interview:
HerStory (HS): How did the AWS DeepRacer Women’s League India come about?
Vaishali Kasturae (VK): We announced the League’s entry into India on International Women’s Day this year. AWS is focused on putting machine learning (ML) in the hands of every developer and data scientist. It also recognises the need to encourage and develop more women ML professionals in the deep tech workforce. The AWS DeepRacer Women’s League – India 2021 is a unique, fun, and engaging initiative by AWS in India that encourages women students to get started with ML.
It’s an autonomous miniature car competition with reinforcement learning (RL). Women are typically shy and risk-averse when it comes to new things. This is an excellent platform for them to learn, adopt ML through a national event. It allows them to work collaboratively on ML solutions and opt for broader and bigger careers in technology.
HS: Tell us about the process, from start to finish.
VK: After the announcement, we shortlisted applications from women that came from different parts of the country. Women were taught machine learning in collaborative labs, test and later deploy their models. Fifteen best performers entered the national community race, and then the grand finale, where three winners were chosen.
Essentially, the contest includes the fastest laps done using ML on miniature cars. The separation between the top three spots was just a fraction of seconds. The competition was intense. Helen Thomas, a PhD scholar from the Indian Institute of Science, Bangalore, was the winner. At the same time, Surbhi Agarwal, a BTech student from Visvesvaraya National Institute of Technology, Mumbai, and Kashish Bansal, a BTech student from IIT-Indore, were the runners-up.
There were close to a 1,000 women who participated in the league from all parts of India, including Tier 2 and Tier 3 cities.
![Vaishali Kasturae](https://images.yourstory.com/cs/4/8e7cc4102d6c11e9aa979329348d4c3e/Imageu182-1627987037677.jpg?fm=png&auto=format)
Vaishali Kasturae, India leader for Strategic Projects, AWS
HS: This brings us to the fact that more needs to be done at the grassroots level to introduce women to tech roles. What is your take on this?
VK: When you look at women entering the workforce, it seems in a ratio of 50:50. But if you look under the hood and see how many women are showing the courage to raise their hands and work on emerging technologies, like deep tech, AI, and ML, the gap is still huge. So a lot needs to be done both at the grassroots level and even when they enter the workforce.
We need to encourage girls from a very young age in schools and colleges to opt for STEM programmes. I believe women have this inhibition in their minds, even when they are taking electives or pursuing further education, that science is a problematic field.
We need to create a level playing field, encourage them to participate, enhance their knowledge, and give them the edge. Programmes like DeepRacer were made and designed to provide exposure to young girls. We must give them the tools, and these kinds of programmes allow them to experiment, fail, and eventually learn and then adopt.
HS: How can organisations retain good women talent in the tech force? Many seem to drop out after starting brilliantly.
VK: When you look at large organisations doing massive hiring, women join the workforce, and in maybe four or five years, when they typically start moving up the organisation, they tend to drop out.
Others at this stage stop raising their hands for demanding jobs. They believe that this will be an arduous journey and don’t say yes to complex tasks because they think they will need to work more hours.
I think organisations need to allow women to experiment and give them the platform to collaborate, occasionally fail, and encourage them to raise their hands. Tell them, “We’ve got your back; we will support you, train you, give you the platform, and perhaps even push you a little bit in taking up new technologies your career journey”.
HS: What have been your biggest successes and challenges?
VK: My career, as I explained earlier, panned out in three pivots, and I think what has worked for me is constantly trying to learn and be curious. Among the Amazon leadership principles, the ability to adopt new things is one. It has stood me in good stead, and I am constantly hungry to learn more, which has served me well.
Also, people often say that there is a glass ceiling. If you don't raise your hand and ask for the next hot job, nobody's going to come to you and ask you to take up the role. But I like to tell women that the ceiling is not as hard as the floor is sticky. Have the courage and the confidence even if you are just 60 to 70 percent ready; you will have the grit and tenacity to cover the rest.
HS: Where do you see the AWS DeepRacer Women’s League go from here?
VK: This is just the beginning for India as the League is a vast event globally. My vision is to train thousands of women in ML and put it in the hands of every woman in India.
Edited by Teja Lele