Pi School of AI — Experience
A hands-on mentoring program for people looking to dive into the world of AI
In the digital era, where data reigns supreme and technological advancements reshape our world daily, the fields of artificial intelligence (AI) and data science have become increasingly exciting. From powering predictive analytics to revolutionizing healthcare and driving autonomous vehicles, the applications of AI are boundless, promising to redefine how we work, live, and interact with our surroundings.
As the demand for AI and data science expertise skyrockets, so do the plethora of resources available to aspiring practitioners. From online courses and tutorials to vast libraries of research papers and open-source tools, the abundance of information can be both a blessing and a curse. For many, diving into the world of AI and LLMs can feel like an odyssey, fraught with uncertainty and the daunting task of sifting through endless streams of knowledge.
In such a landscape, finding a foothold can seem like a huge challenge. If you resonate with any of what I have mentioned above, then read on because I will delve deep into an opportunity that I came across which helped me break into AI and might do the same for you!
What is the Pi School of AI?
The Pi School of AI is a full-time 8-week program organized by the Pi School, dedicated to nurturing and training AI professionals to work on real-world AI problems. Their focus is on imparting hands-on technical experience by working on a wide array of problems that help the fellows in connecting academic theory with practical application. The fellows work towards finding solutions to complex problems using cutting-edge AI technologies in the field of healthcare, retail sector, public policy, finance and many more. Apart from technical skills, the fellows get to improve their communication, business acumen, and soft skills.
In their own words, the following lines explain the fellowship really well!
Merit First. Top engineers get in for free.
Learn by doing. Rather than listening to lectures, run into issues and solve them. Desks and environment are organized to support small project teams, agile co-development, interactions with mentor.
Real world projects, no simulations. Our partners sponsor top engineers to solve real challenges. This may convert into the best job you ever had.
Format of the Program
The School of AI is run twice a year, where they host a batch of engineers and data scientists from all over the world to train them to solve nuanced technical challenges using AI and Machine Learning. Each fellow receives personalized coaching and guidance from an expert coach and a technical mentor. They can learn and specialize in any domain of AI through the workshops conducted at Pi School. The fellows then apply their new skills on an industry project provided by various world-leading tech companies such as Google, Facebook and Amazon, and fast-growing startups.
Generally, the projects are assigned to groups of 2–3 participants and each project is sponsored by an industry partner. The fellows work on the project using a structured but fast-paced approach in about 8 weeks, going from prototype to finished product with multiple iterations of testing and designing AI models. In my batch, we were a group of 3 people, specializing in different domains who were brought together to solve a problem in the retail sector.
Through this program, the partner organizations can unravel the potential of using data science and AI to support their businesses and solve complex technical problems, while the fellows can hone and apply their analytical skills and learn from full-time mentors coming from industry and academia.
Application Process
The screening process for the Pi School of AI can be quite selective, as evidenced by my own experience in which over 350 individuals applied for my batch, yet only 10 were chosen (~2.8% acceptance rate). It’s not to dissuade potential applicants, but rather to emphasize the importance of putting your best foot forward. The application process is great way for you to dive deeper into your work and assess why you would be a good fit for the program. The process is aimed at getting to know you better.
The following section gives a detailed overview of the process and what to expect. I’ll also share some tips on how to stand out in the application process.
Application Form
The application for the fellowship opens twice a year. There is no fixed timeline for the release of the forms (as it depends largely on the project availability) but from what I have observed, you can expect it to open once around end of March and then around start of October. The link to the application form can be found on the program website.
Tip: Regularly checking the page for new intake will ensure that you never miss out. You can check once a month to stay on top of the application timelines.
The form requires the applicant to explain their past work in the field of interest in detail along with their motivation to be a part of the program. Do remember that you will be asked questions about what you wrote in your application form in further rounds. It might be helpful to write down all the questions in a word file and review them before submitting. You are also required to submit your transcripts. I don’t know how much my grades mattered but if you’re reading this in your first/second year of university, it might be helpful to work on your grades a little bit as well.
Tip: Complete the first draft for all the answers and then review them after a week’s gap. This would give you a fresh perspective and you will be able to find places which can be improved.
Interview Round
After about a week or so, I got an email to schedule my interview. There were three panelists who are ML Scientists at Pi School and they majorly asked me questions around the project-related skills and my past experience. It was a really fun conversation and they made sure that I was comfortable before the interview and at no point made me feel like they were evaluating me. There were no “trick questions” (read: leetcode) which would push me into making a mistake and give them a reason to reject me. It was purely about knowing more about me, my work and past projects.
This round will also entail a code walkthrough of a past project. I picked a project around forecasting to showcase my technical skills. Since it was a code walkthrough, I explained how I built the project and they kept asking me questions about why I used a certain model over the other or how I solved a potential problem. There was an additional discussion on Forecasting (since it was related to my project) and LLMs. Towards the end, I asked a few questions about the program and about the working style.
My advice for this round is to be thorough with whatever project you have built. It doesn’t matter what frameworks or languages you use as long as you can explain why and how you built the project. Additionally, some of my colleagues talked about an algorithm implementation since they came from a more mathematical background. I showed a full-stack project because I had a strong programming background.
Conclusion
After about 2–3 weeks, I heard back from them that I had been accepted into the program and I was overjoyed!
I hope this application process walkthrough would be helpful. Irrespective of the outcome, if you’re reading this article, you should apply for the program because the application process itself is a really great experience. It will help you reflect on your work and your motivation to pursue data science.
Work Experience
This blog would be remiss without me talking about my experience during the program. The program commenced with 10 fellows coming from a variety of backgrounds and many different countries. This was one of the best things about the fellowship. I was able to meet and become friends with people across the globe. The fellows were divided into teams of 2–3 people, working on 4 different projects. My team comprised of people from India and Italy. We had two coaches and a mentor throughout the program.
The environment was incredibly collaborative, with everyone eager to help and share knowledge. This spirit of cooperation made tackling complex challenges a collective effort, and it fostered a supportive learning community. We enjoyed a perfect balance of work and fun, making the journey both productive and enjoyable. The campus itself was stunning, providing a beautiful backdrop for our daily activities and inspiring creativity.
Amidst our project work, we participated in insightful lectures and deep dive sessions, which expanded our understanding and skillset. These sessions were complemented by opportunities to interact with other startups and their founders, allowing us to gain diverse perspectives and insights into the industry.
Adding to the experience was the delightful la cucina italiana (Italian cuisine) served on campus. The meals were a culinary treat, and the unlimited coffee kept our energy levels high. This combination of great food, constant caffeine, and a vibrant, supportive community made my time at Pi School of AI an unforgettable and enriching chapter in my AI journey.
Teamwork
My Pi School experience wouldn’t even be 1/10th complete if I didn’t talk about my amazing team! I could talk for hours about what a great experience I had with them but let me first introduce them:
- Hari Prasad — AI Researcher with a Masters in Artificial Intelligence
- Valerio Calà — Statistician with a Masters in Economics and Finance (also my Italian language teacher)
- Marcello Politi — Machine Learning Scientist at Pi School, Crypto enthusiast and Master’s in Computer Science. Our project manager and coach.
- Vijayasri Iyer — Machine Learning Scientist from India with a Master’s in AI. She was also our coach.
- Adrian Buzatu — Staff Data Scientist with a Postdoctorate in Particle Physics and our project mentor. (Fun Fact: He was on the founding team of Higgs Boson at CERN.)
- Myself — The ML Engineer and the person with the least number of academic degrees 😂
Of course, there were many other people who contributed to the success of our project! A good team is the most essential aspect of a successful project and I had the privilege of working with some of the best people. Our very diverse backgrounds proved to be one of the major factors in the success of our project. This, combined with the fact that each of them was always ready to help out one another, made us work together very productively.
While we worked really hard, it wasn’t the only thing we did. I think we were definitely the most chatty team as well. We were always willing to learn about each other’s culture, language and academic background. I know so much more about Italy and south of India that I could probably win a trivia competition.
Like I said, I could talk for hours about how much fun we had as a team but the blog is already too long but I am glad that you have read so far.
Final Pitch
The event concludes with a final pitch day where all the fellows get together to talk about their projects and the work they have done. We also network, drink beer and have pizza to commemorate all our hard work.
You can find my project topic below:
Revolutionising Retail with AI-Enhanced Stock Management
Discover how cutting-edge AI technology is transforming retail inventory management. This groundbreaking project leverages advanced AI algorithms to optimise stock levels, ensuring maximum sales and minimal surplus. The solution promises to be a game-changer in supply chain efficiency, setting a new benchmark in integrating tech innovation with practical business strategies.
Here is a video from the final pitch of our solution, if someone is interested:
Conclusion
The Pi School of AI has been an enlightening journey for me. I grew as a data scientist and as a person, all thanks to the program. I have picked up a lot of new skills over these last months which I will be able to apply to other projects. I was also able to connect with startup founders, academics and people in the industry; which was also one of my goals.
I recommend this program to budding Data Scientists and AI Engineers who’re looking to apply their skills to problems that matter and learn a lot in the process.
