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 | 49 | Founded | 4 years ago | Last Issue | 13 days ago |
| Active | |||||
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mlfrontiers.substack.comLatest Issues
Building a Financial Foundation Model From Scratch on a MacBook Pro
Dat Tran, Unsplash
Financial foundation models (FFMs) — LLMs, but trained on transaction sequences instead of word sequences — are one of the latest emerging trends in AI. Results shared publicly so far have been strong: improvements like...
The Rise of Financial Foundation Models (FFMs)
Transformer-based foundation models have revolutionized language modeling, vision modeling, and, most recently, recommender systems. Their application to financial ML problems such as fraud detection, credit scoring, or cashflow prediction...
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...
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Physics PhD turned ML Engineer
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