Find out more about Nabil’s background, his missions and the projects he works on at DCbrain.
What studies have you done and how did you come to DCbrain?
I studied Software Engineering at the École Nationale Supérieure d’Informatique in Algeria.
In 2017, I completed an end-of-study project, part of which dealt with machine learning. That’s when I discovered this field and began to take an interest in it.
I therefore decided to continue my studies by completing a specialised master’s degree in data science and machine learning at Paris Saclay.
I joined DCbrain for an internship, with the desire to work on generative models, reinforcement learning and learn how to work with clients. I never left.
What is your job and what are your missions?
I am Data scientist at DCbrain. My missions also correspond to the job of Machine Learning Engineer.
My main mission is to make what is done in Machine Learning and data science accessible to customers.
For me, Machine Learning is the set of techniques and algorithms that teach computers to mimic humans.
What tools do you use?
I mainly use two tools:
-PyTorch: It allows you to build machine learning models.
-Kubeflow: Software that automates data science pipelines and makes Machine Learning models accessible as a service to customers.
Our learning machine models are trained and deployed on Kubernetes clusters with high scalability to optimise response times and speed up the training of models on GPUs.
What projects are you currently working on?
A short while ago, I was working on a project for a customer in logistics. The aim was to optimise their production and cement transport.
Currently, I am creating an automatic forecasting module for customers who want to make predictions on their data (consumption data for example).
The aim of this project is to make forecasting and anomaly detection modules available to customers.
Generally speaking, during a customer project, the technical profiles, including me, are involved from the very early stages of sales. They join the calls by listening to the customer to understand the need, and have a better visibility on potential technical complicities.
They then analyse the data received, and contact the customer if necessary to better understand their data.
Why work with DCbrain?
I enjoy working with DCbrain for three main reasons:
- The competence of the team: whatever the technical field, each employee has his or her own specialisation and expertise in his or her field. I know that if I have a question, I will get an answer.
- The freedom: employees are free to keep a technological watch on new tools that come out, and have the freedom to explore and test these new features. If they find that the tool would be beneficial to the Company, they can propose it and decide to implement it. In fact, I was the one who suggested using Kubeflow and PyTorch.
- The goodwill of the team.
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