Reflections on writing about Data Science: 2024 Posts

This year, I decided to start writing and sharing knowledge about Data Science. My goal wasn’t to become an influencer on LinkedIn/Medium, but rather to motivate myself to read more and think deeply about the content. Writing, even if just a simple summary, helps reinforce learning (pun not intended).

For those who missed it, here is a list of posts I published throughout the year:

Weekly Reading: AI Agents: A post where I discuss the popular topic of AI agents.

Weekly Reading: Meta’s Approach to Machine Learning Prediction Robustness: A brief post highlighting Meta’s material on robustness in machine learning models, a topic also debated at iFood.

Designing a Machine Learning System: A discussion about how an AI model is merely a part of a larger system, inspired by an excellent post by Chip Huyen.

Recommended Reading: How I Use “AI”: Discussing the role of genAI in professional productivity, based on tips from the author Nicholas Carlini.

Transforming Data into Art: The Evolution of Cover Selection at Netflix: My favorite post of the year, combining my passion for movies, series, and AI. It was meant to be just a post on Causal Machine Learning and turned into an exploration of Netflix’s evolution in choosing covers/artwork.

Thoughts on: RAG, Hybrid Search, and Rank Fusion: Reflections on important topics for my professional practice, such as RAG and Rank Fusion.

Conclusion

There were only a few posts, but I enjoyed the process of writing them. I’m not a writer, but these activities helped me read and reflect more on each topic. I hope they were helpful to others as well.

Stay tuned, in 2025 there will be more!