Machine Learning Frontiers
Samuel FlenderReal-life lessons on ML theory, practice, and careers from a seasoned ML engineer. This is the stuff you won't learn in "standard" ML curricula.
| Platform |  | Pricing | Freemium | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 47 | Founded | 4 years ago | Last Issue | 2 months ago |
| Active | |||||
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mlfrontiers.substack.comLatest Issues
LLM Evaluation: The New Bottleneck in AI
It is 2026, and LLM-generated content is everywhere, from brainstorming with ChatGPT to web search with Gemini, personal note editing with Notion AI, and code generation with Claude.
The question is no longer “how do we build LLMs that can...
The LLM Judge Controversy
The judge will see you now.
For a long time — at least since the 1960s — the gold standard for evaluating information retrieval (IR) systems, the backbone of search engines, has been the Cranfield paradigm. The paradigm suggests that in or...
The Rise of LLM-Infused Embedding Models
Text embeddings are fundamental components of numerous natural language processing and information retrieval applications, including web and on-device search, question answering, recommender systems, classification systems, risk and complia...
Model Serving Engineering: A Primer
The journey from an offline-trained model into a real-time prediction service
From an academic point of view, the goal of ML research is to build a model that predicts a real-world phenomenon better than the current state of the art. Conse...
Feature Infrastructure Engineering: A Comprehensive Guide
While much of the ML literature focuses almost exclusively on model architecture, features are often taken for granted. Yet in many real-world applications, it’s improvements in features — not models — that can drive the majority of perform...
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Physics PhD turned ML Engineer
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