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 — start reading.
Machine Learning
Distill — Clear explanations of machine learning
Beautifully interactive deep-dives into ML concepts — attention, optimization, GNNs, RL. Best when you want to truly understand something visually.
Read →Towards Data Science — Machine Learning
The biggest ML publication on Medium. Practical tutorials, end-to-end projects, and career write-ups updated daily.
Read →Sebastian Raschka — 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 →Lilian Weng's Log
Long-form survey posts that have become canonical references — attention, prompt engineering, agents, reward hacking.
Read →Machine 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 →Deep 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 →Andrej Karpathy's blog
Classics like 'A Recipe for Training Neural Networks' and 'The Unreasonable Effectiveness of RNNs'. Few posts, all gold.
Read →Hugging Face Blog
Walkthroughs of new open models, fine-tuning recipes, RLHF, quantization, and inference tricks — from the people shipping them.
Read →Chip Huyen — 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 →Anthropic Research
Frontier LLM research — interpretability, alignment, constitutional AI, agent capabilities. Heavier reading but the source of record.
Read →OpenAI Research
Papers and posts behind GPT, function calling, and reasoning models. Pair with the API documentation when shipping with them.
Read →Data Science & Analytics
Towards Data Science
The default Medium publication for data science. Filter by 'Editor's Picks' to avoid the noise.
Read →KDnuggets
One of the oldest data-science publications. Weekly digests, cheat sheets, and career advice for analysts and scientists.
Read →Stitch Fix Algorithms
Industrial DS done right — experimentation, causal inference, recommendations. Read for how DS actually shows up at a real company.
Read →Variance Explained
Tidy-data thinking, R + Python deep dives, and statistical reasoning by a former DataCamp / Heap chief data scientist.
Read →Eugene Yan
Practical essays on ML systems, recommender systems, applied LLMs, and how senior ICs work. Perennially shared on Twitter for a reason.
Read →MLOps & ML Engineering
Made With ML
A full free curriculum on going from notebook to production — pipelines, monitoring, CI/CD for ML, and testing.
Read →Neptune.ai Blog
Tool-agnostic write-ups on experiment tracking, model registries, drift detection, and choosing infra for ML teams.
Read →Weights & Biases — Fully Connected
Reports, interviews, and reproducible notebooks. Strong on training-pipeline best practices and evaluation.
Read →MLOps Community Blog
Practitioner voices on real MLOps problems — vector stores, feature stores, eval, and on-call for ML systems.
Read →Engineering 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 →Uber Engineering
Michelangelo (their ML platform), forecasting, geospatial ML, and large-scale data engineering.
Read →Airbnb Engineering & Data Science
Search ranking, pricing models, experimentation, and how Airbnb structures its DS function. Excellent for interview prep.
Read →Google Research Blog
The latest from Google's research arm — Gemini, robotics, health AI, responsible AI.
Read →Meta AI Blog
Llama releases, recommender systems, multimodal models, and infrastructure posts from FAIR.
Read →Spotify Engineering
Music recommendations, large-scale embeddings, audio ML, and the ML platform behind Discover Weekly.
Read →Statistics & Research
Andrew Gelman — Statistical Modeling
Bayesian thinking, causal inference, and merciless critique of bad statistics in published papers. Sharpens how you reason about data.
Read →Cross Validated — Most Voted
Top-voted Q&A on statistics and ML. The highest-voted answers are often a faster path to understanding than textbooks.
Read →The Gradient
Long-form essays surveying AI research areas — accessible enough to be readable, deep enough to be useful.
Read →AI Snake Oil
Princeton researchers cutting through AI hype. Read before believing the next viral benchmark claim.
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