Personally, I find it beneficial to keep track of the latest research and papers from top AI conferences. Try not to be overwhelmed by the sheer number of papers coming out every day, it is helpful to extract important methods & insights in the paper without getting lost in jargon or hype. Another tip that I find useful is to write clean, reproducible, version-controlled code, ideally with a sense of experiment tracking, model versioning, and deployment awareness.

28 April, 2025 20:48

Thanks for sharing! I completely agree - staying updated and maintaining clean, reproducible code makes a big difference.

28 April, 2025 23:36

Rita L. Assistant Professor

Hi Mohamed,
Thank you for your question! While I haven’t directly worked on NLP/ML projects, my current role as a Process Mining expert has given me a strong appreciation for the skills and mindset needed to succeed in any data-driven field. One habit I consider essential—especially in fast-evolving areas like NLP and machine learning—is a commitment to continuous learning.

Staying curious and consistently investing time in expanding your knowledge—whether it’s exploring new tools, understanding emerging methodologies, or adapting to shifts in technology—is absolutely vital. In a rapidly changing environment like Nokia’s Emerging Businesses team, where innovation moves quickly, the ability to learn, unlearn, and relearn is just as important as any technical skill.

Even though my focus has been on process mining, many of the same habits apply across domains: proactively following trends, experimenting with new platforms, and learning from both successes and failures. These are the foundations that prepare you to take on increasingly complex challenges, whether in process optimization or advanced AI initiatives.

Hope I could help you and wishes of a great and bright future!

29 April, 2025 11:07