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Applying Machine Learning to the Art World: Mamdouh’s story

Coming from the Academic World, Mamdouh was on the lookout for exciting challenges, so he joined Le Wagon's 9-week program to learn data science skills and launch his career in the field. Today, he uses Machine Learning to help artists replicate their art pieces. Discover his journey!

Tell us about your background and what inspired you to learn data science

I did a bachelor's in Civil Engineering and pursued my studies up to the Ph.D. My research project was about wastewater treatment. 

During the pandemic, I couldn’t continue my research so I had a lot of time to reflect. I knew I wasn’t going to finish my Ph.D. because I lost the sense of growth and wanted to focus on something new. 

I had skills in math and statistics, participated in a few hackathons, and I’m a curious person. I chose to learn data science because there’s a huge room for growth and it was out of my comfort zone. 

Why did you choose Le Wagon and what did you think of your bootcamp experience? 

There are so many bootcamps on the market. I chose Le Wagon because of the great feedback I heard from alumni and for the condensed format of only 9 weeks. 

I enjoyed the format of the program because I felt like I was working at a tech company. I learned new concepts but was also trained for the workplace environment.  

We had a lecture at 9 AM and then we got to work on some technical challenges before closing the day with a livecode. This schedule made me feel in a workplace environment where I’d get to code all day and get help and insights from senior developers, Le Wagon’s teachers.

How was your job search after graduating? 

I was hired at Acrylics Robotic shortly after graduating. 

The Career Week gave me a good structure to revamp my CV and prepare all the material I needed to market myself and apply for jobs. It was great to get help from the TAs and other students. 

What is Acrylics Robotics and what is your role there?

Acrylics Robotics helps artists replicate their paintings with a suite of robotic tools. In other art fields (music, literature, etc), it’s easy to make a piece and sell it everywhere because there’s an infrastructure for mass production. But this doesn’t translate for paintings. 

Our job is to extract the “DNA” of the painting - meaning the motion required to make it - and to give a set of instructions (stroke trajectory, palette color, etc) to a robot to replicate it. 

I work on a Machine Learning project where the robots wouldn’t need instruction sets but could guess how to replicate a painting by simply looking at an image. This could allow anyone to create a painting they like without having the skills to do it. With this kind of technology, you could say “I want this image painted in the style of the Mona Lisa”.It’s an interesting way of democratizing art. 

What do you enjoy the most about working in Data Science?

Working in Data Science has many common points with my experience as a researcher. To build a Machine Learning model I start by reading the literature and understanding what already exists out there. Then I implement experiments, learn from them, and restart that loop. 

The biggest difference is that the loop closes much faster. 

When I was doing research, it would take weeks or months to get results. In Machine Learning, I get feedback immediately and can iterate on it quickly. 

I also enjoy working at a startup because of the flexibility and room for learning it gives me. As the only developer on the team, I get to build stuff I’ve never done before. 

How do you use the skills you learned at Le Wagon?

The bootcamp gave me a great starting point to get technical skills and understand what I need to focus on to keep growing.

The modules that I found the most interesting are Deep Learning and MLOps. With MLOps, I learned how to deploy machine learning models and put them into production. 

During project weeks, I learned to work in a team, communicate with people, and get used to tight deadlines. 

We only had 2 weeks to build our final project! This is a similar experience to working at a startup. The deliverables are tight and you might have to work extra hard for a few weeks to complete them.

You’re also a teacher at Le Wagon. What do you enjoy about teaching? 

Part of the reason why I started a Ph.D. is that I enjoyed teaching at university. When I was an undergraduate student, my appreciation for a course didn’t necessarily come from the content but the way it was presented. 

I think that if you’re passionate about something, it’s your responsibility to get others excited about it too. So I teach the modules that I enjoy the most such as Machine Learning, Deep Learning, and MLOps. 

Any advice for those who want to get into data science? 

Be curious. Data science is a rapidly evolving field so you need to stay up to date. Curiosity is what drove me to start learning and what keeps me going. 

You should also build intuition. It can be intimidating to learn Machine Learning when you just look at the math. Try to understand what is happening before explaining things mathematically. Looking at online resources such as videos, blogs and technical articles can be helpful to get familiar with the field.
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