Blogs worth reading
A curated list of the best AI, ML, and Data Science writing from Medium, personal blogs, and big-tech engineering teams. Stop scrolling Twitter โ start reading.
Machine Learning
Distill โ Clear explanations of machine learning
Beautifully interactive deep-dives into ML concepts โ attention, optimization, GNNs, RL. Best when you want to truly understand something visually.
Read โTowards Data Science โ Machine Learning
The biggest ML publication on Medium. Practical tutorials, end-to-end projects, and career write-ups updated daily.
Read โSebastian Raschka โ Ahead of AI
Author of 'Build a Large Language Model (From Scratch)'. Deep, paper-grounded explainers on LLMs, fine-tuning, and ML research trends.
Read โLilian Weng's Log
Long-form survey posts that have become canonical references โ attention, prompt engineering, agents, reward hacking.
Read โMachine Learning Mastery
Hands-on tutorials with runnable code. Great for going from 'I read about it' to 'I built it' in scikit-learn, Keras, and PyTorch.
Read โDeep Learning & LLMs
The Illustrated Transformer
If you only read one post on transformers, read this one. Illustrated explanations of BERT, GPT, attention, and embeddings.
Read โAndrej Karpathy's blog
Classics like 'A Recipe for Training Neural Networks' and 'The Unreasonable Effectiveness of RNNs'. Few posts, all gold.
Read โHugging Face Blog
Walkthroughs of new open models, fine-tuning recipes, RLHF, quantization, and inference tricks โ from the people shipping them.
Read โChip Huyen โ Designing Machine Learning Systems
ML systems, production GenAI, RAG, agents, and the gap between research and reality. A must-read for ML/AI engineers.
Read โAnthropic Research
Frontier LLM research โ interpretability, alignment, constitutional AI, agent capabilities. Heavier reading but the source of record.
Read โOpenAI Research
Papers and posts behind GPT, function calling, and reasoning models. Pair with the API documentation when shipping with them.
Read โData Science & Analytics
Towards Data Science
The default Medium publication for data science. Filter by 'Editor's Picks' to avoid the noise.
Read โKDnuggets
One of the oldest data-science publications. Weekly digests, cheat sheets, and career advice for analysts and scientists.
Read โStitch Fix Algorithms
Industrial DS done right โ experimentation, causal inference, recommendations. Read for how DS actually shows up at a real company.
Read โVariance Explained
Tidy-data thinking, R + Python deep dives, and statistical reasoning by a former DataCamp / Heap chief data scientist.
Read โEugene Yan
Practical essays on ML systems, recommender systems, applied LLMs, and how senior ICs work. Perennially shared on Twitter for a reason.
Read โMLOps & ML Engineering
Made With ML
A full free curriculum on going from notebook to production โ pipelines, monitoring, CI/CD for ML, and testing.
Read โNeptune.ai Blog
Tool-agnostic write-ups on experiment tracking, model registries, drift detection, and choosing infra for ML teams.
Read โWeights & Biases โ Fully Connected
Reports, interviews, and reproducible notebooks. Strong on training-pipeline best practices and evaluation.
Read โMLOps Community Blog
Practitioner voices on real MLOps problems โ vector stores, feature stores, eval, and on-call for ML systems.
Read โEngineering Big Tech Blogs
Netflix Tech Blog
How Netflix builds personalization, A/B testing platforms, and ML infra at scale. Required reading for senior DS/ML candidates.
Read โUber Engineering
Michelangelo (their ML platform), forecasting, geospatial ML, and large-scale data engineering.
Read โAirbnb Engineering & Data Science
Search ranking, pricing models, experimentation, and how Airbnb structures its DS function. Excellent for interview prep.
Read โGoogle Research Blog
The latest from Google's research arm โ Gemini, robotics, health AI, responsible AI.
Read โMeta AI Blog
Llama releases, recommender systems, multimodal models, and infrastructure posts from FAIR.
Read โSpotify Engineering
Music recommendations, large-scale embeddings, audio ML, and the ML platform behind Discover Weekly.
Read โStatistics & Research
Andrew Gelman โ Statistical Modeling
Bayesian thinking, causal inference, and merciless critique of bad statistics in published papers. Sharpens how you reason about data.
Read โCross Validated โ Most Voted
Top-voted Q&A on statistics and ML. The highest-voted answers are often a faster path to understanding than textbooks.
Read โThe Gradient
Long-form essays surveying AI research areas โ accessible enough to be readable, deep enough to be useful.
Read โAI Snake Oil
Princeton researchers cutting through AI hype. Read before believing the next viral benchmark claim.
Read โ