I’m currently a machine learning professional and have been working in Silicon Valley for about 15 years. I’ve been with big companies, like Amazon Web Services and Apple, and startups focused on data labeling and machine learning.

I have been interested in AI for Good-type work for the majority of my career. Almost 20 years ago, a good friend of mine lost a child to SIDS (sudden infant death syndrome). As a result, I started volunteering with Seattle Children’s Hospital, helping to understand the causes and preventative steps that could be taken to reduce the risk of SIDS. This volunteer engagement led to the opportunity to create the Microsoft AI for Good Lab. I could have never imagined the scope and scale of the work we’re fortunate to do today. From improving health outcomes to biodiversity conservation and humanitarian response, the relevance and need for AI in every sector has never been more pronounced.

AI can seem like a dream profession: Machine learning engineers and data scientists are in-demand and well paid. On top of that, many find the day-to-day work of building models, tuning algorithms, and analyzing data to be deeply satisfying. But the field is also competitive and fast-paced. Job seekers, especially those early in their careers, are often confused about the best way to get hired.

Luis is a lifelong mathematician and a machine learning scientist by trade. He has taught courses on probability at Université du Québec à Montréal and the University of Michigan, built algorithms for Google, taught AI at Apple, and currently works on language models for Cohere. His YouTube channel, Serrano Academy, is known for its approachable, bite-sized explanations of a variety of math and machine learning topics. And somewhere amid all of that, he found time to write a book: Grokking Machine Learning. 

I joined the DeepLearning AI Community because here in El Salvador, there were not many communities that talked about this topic, nor is it taught in our universities. I saw that DeepLearning.AI could help me to spread knowledge and connect with people interested in this field. 

I felt that DeepLearning.AI is a great platform to share the work of Singapore’s AI researchers and students and also to help them connect with the wider global community. Furthermore, with new exciting developments happening almost on a daily basis in the field of AI and Data Science, the DeepLearning.AI community offers a convenient and effective channel for us to learn from each other.

I realized how deeply ingrained AI is in our everyday lives. Most people may not be aware of that, hence I wanted to bring that awareness and knowledge to them. By becoming an event ambassador, I am able to help spread AI awareness to others, as well as continue to learn from the AI experts who I host for the panel discussions. It amazes me how much people in the larger community are hungry to learn and how much appetite for AI there is out there.

I am a Data Scientist specializing in Space Systems Engineering. Part of my work is done at the European Space Agency. My job is to make space design and operations better with machine learning. For instance, developing anomaly detection systems to alert spacecraft operators about an abnormal satellite behavior. I also work on forecasting models, such as to predict the onboard power consumption, the temperatures of different equipment on the satellite, or to predict the remaining useful life of components. Other applications relate to natural language processing (NLP), such as question answering systems to help space systems engineers or flight control teams to gain quicker access to reliable technical information.

I first attended a Pie & AI event organized by David Quintanilla, an AI ambassador here in San Salvador, and he explained what Deeplearning.AI and Pie & AI are. I liked the idea of becoming an ambassador because I’m a content creator and the role would help me improve the quality of my content. I was also interested because it would help me meet more people around the globe who are passionate about software development.

I wanted the Oceania region to be abreast of the latest developments from DeepLearning.AI and make AI knowledge available and easily accessible for the event attendees. Being geographically quite far away from the rest of the world, sometimes it becomes a challenge to keep up to date with the latest developments in AI. I wanted to play a small part in it by connecting AI enthusiasts and specialists from Oceania to the rest of the world.

Joining the DeepLearning.AI Ambassador community was one of the best things I did in the early days of my AI learning journey. The events I hosted made a big impact not only on me, but on other people as well. I sometimes meet people who tell me that events I hosted helped them to make a career in AI. The other day on campus, I met someone who told me that he chose to major in machine learning after watching the event I hosted with Santiago Valdarrama. 

I have always been active in organizing research seminars and events in my local setting, but I wanted to extend these activities to a global audience to achieve more diversity. Pie & AI events in a hybrid setting (local + virtual) seemed like the perfect way to do so.

I am the CTO and co-founder of Marvik, a software development firm specialized in machine learning. We help companies identify opportunities to leverage their data. I love the challenge of solving problems that no one else has solved before.

Pie & AI reaches underserved communities and highlights the importance of data science, artificial intelligence, and machine learning. As an undergrad, I’m always enthusiastic about learning and exploring various technologies. AI and its capabilities caught my eyes right away. As an IoT student ambassador for my college campus, I always aim to connect AI and IoT, making something new out of it. As the event ambassador of DeepLearning.AI, I get the opportunity to network with people from AI and ML backgrounds. I was fortunate to come across the ambassador opportunity and was glad to start organizing events.

Since 2019, Pie & AI ambassadors have hosted over 700 events. Each year we celebrate their efforts.

Dr. Benjamin Harvey, CEO of AI Squaredhas used machine learning to investigate environmental problems, aid cancer research, and probe the damage caused by national security leaks

Jared Webb, chief data scientist at BlueConduit, discusses his transition from academia to business, how he navigates the challenges of working with government data, and what he looks for in new hires. 

In the mid-2000s, AI was still just a curiosity to the world at large. At Stanford University, however, one of the most popular classes on campus was Andrew Ng’s CS229 machine learning course. Enrollment was frequently too large to fit in the classroom, yet he wanted even more people to be able to master machine learning.

So, working with a few students, he created an online Machine Learning course that could be taken by anyone with an internet connection and a desire to learn. The rest is history. Coursera launched in 2012 with Machine Learning as its flagship title. It was also the platform’s most popular, with almost 5 million enrollments.

This year, to celebrate the course’s 10 year anniversary, DeepLearning.AI and Stanford Online released a successor — the Machine Learning Specialization. Andrew spoke with us about how the new Specialization improves on the original, who should take it, and how it fits into the modern AI builder’s career arc.

Kulsoom Abdullah pivoted from network security into a data science role after taking Andrew Ng’s original Introduction to Machine Learning course from Stanford in 2013. A self-described learning addict, she has taken more than 10 online courses, most recently the Deep Learning Specialization. Outside of work, she’s a fitness fanatic, and was the first competitive weightlifter to compete professionally for Pakistan — after winning a fight to change the dress code to allow religious head coverings. On top of all that, she’s a travel

Lorenzo Ostano first encountered machine learning and data science as a business analyst shortly after graduating from college. He dove into learning, and soon landed work as a machine learning engineer. After working several years for a variety of consulting companies, he recently took a job as a software engineer. He spoke to us about why he believes traditional computing skills are important to deploying enterprise machine learning applications.