photo by Maria Kostyleva (IG:masha_and_film)
🎯 Production-Ready Code
Even the best of the best ML model can fail because of poor engineering practices. Data Science is a branch of Computer Science. It's time to go to the roots and learn the best practices of software development.
🎯 Product thinking
Shift your focus beyond model metrics—think about real user impact. What problem are we truly solving, and how does our solution make a difference?
🎯 Fluency in languages
Not coding languages, but the languages of product, business, and data science. A brilliant technical solution means nothing if you can't communicate its value and get stakeholders on board.
🎯 Classical ML
Not every new research paper needs to be implemented. Often, strong fundamentals outweigh the latest cutting-edge techniques.
🎯 Foundational Models
LLMs are incredibly powerful, but without a solid understanding of how they work, you can run into trouble fast.
🎯 End-to-end solution
Model in the jupyter notebook does not have any value. Learn how to build API out of it. Also you don't have to be UX designer to add a simple UI on top of it.
Bonus points: you know how to deploy your solution on cloud.
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