This 12 months, I made a decision to start out writing and sharing data about Information Science. My purpose wasn’t to turn out to be an influencer on LinkedIn/Medium, however fairly to encourage myself to learn extra and assume deeply concerning the content material. Writing, even when only a easy abstract, helps reinforce studying (pun not supposed).
For individuals who missed it, here’s a checklist of posts I printed all year long:
Weekly Reading: AI Agents: A submit the place I focus on the favored subject of AI brokers.
Weekly Reading: Meta’s Approach to Machine Learning Prediction Robustness: A short submit highlighting Meta’s materials on robustness in machine studying fashions, a subject additionally debated at iFood.
Designing a Machine Learning System: A dialogue about how an AI mannequin is merely part of a bigger system, impressed by a superb submit by Chip Huyen.
Recommended Reading: How I Use “AI”: Discussing the position of genAI in skilled productiveness, primarily based on suggestions from the writer Nicholas Carlini.
Transforming Data into Art: The Evolution of Cover Selection at Netflix: My favourite submit of the 12 months, combining my ardour for motion pictures, sequence, and AI. It was meant to be only a submit on Causal Machine Studying and was an exploration of Netflix’s evolution in selecting covers/paintings.
Thoughts on: RAG, Hybrid Search, and Rank Fusion: Reflections on necessary subjects for my skilled follow, equivalent to RAG and Rank Fusion.
There have been only some posts, however I loved the method of writing them. I’m not a author, however these actions helped me learn and replicate extra on every subject. I hope they have been useful to others as nicely.
Keep tuned, in 2025 there shall be extra!
