Avantages
1. Good place to build strong fundamentals
I got solid hands-on experience with Python, SQL, basic ML, and data cleaning, which strengthened my foundation.
2. Supportive environment for beginners
The team was friendly and approachable, which made it easy to ask questions and learn at my own pace.
3. Exposure to end-to-end ML workflow
I worked on the full cycle—data preprocessing, model building, evaluation, and reporting.
4. Real-time projects instead of dummy datasets
I got to work with actual data, which helped me understand real industry challenges like missing values and data imbalance.
5. Opportunity to automate tasks
I got experience in automating reporting and analytics tasks, which improved my practical Python skills.
Inconvénients
1. Limited exposure to advanced ML/AI tools
The projects were mostly focused on basic ML models and analytics, so exposure to large-scale ML systems or GenAI frameworks was limited.
2. Smaller team → fewer mentorship opportunities
The team size was small, so sometimes I had to figure things out myself without much technical guidance.
3. Project scope was narrow
Many tasks were routine data cleaning, dashboards, and simple ML, so the opportunity to explore more complex pipelines or AI workflows was limited.
4. Tech stack was traditional
Most work was Python + SQL + basic ML. Tools like RAG, LLM APIs, LangChain, and vector DBs weren’t used there.