Towards Data Science
Jul 28
Don’t Just “Throw Adam at It”: Misunderstanding Adam Will Cost You
You "vibe coded" the import. Understand Adam's optimization dynamics, why it fails spectacularly, and how to fix it.
How Wrong Is Your Marketing Mix Model (MMM)?
The article discusses how even a well-constructed Marketing Mix Model (MMM) can produce inaccurate results because the data it relies on lacks essential information. It explores the idea that adjusting the timing of marketing spend, rather than the amount, might correct these inaccuracies. The piece highlights the limitations of MMMs when the underlying spend data is incomplete or misaligned with actual campaign dynamics.
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Towards Data Science
May 29
RAG Is Burning Money — I Built a Cost Control Layer to Fix It
Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without sacrificing answer quality.
Towards Data Science
Aug 11
The Budget Split That Explains Itself
How to diversify a budget without losing the shadow prices that explain the result The post The Budget Split That Explains Itself appeared first on Towards Data Science .
Towards Data Science
Oct 1
Your AI Bill Is a Toll Booth. Stop Paying Twice.
The article titled "Your AI Bill Is a Toll Booth. Stop Paying Twice." discusses how users are unexpectedly paying more for AI services than anticipated, likening the experience to a toll booth where one pays twice. It highlights the unseen costs that can arise when using AI tools and urges readers to be vigilant about their expenses. The piece was first published on Towards Data Science.
Towards Data Science
Jun 11
BI Is Dead, Long Live BI
The true bottleneck was never the analysis. The post BI Is Dead, Long Live BI appeared first on Towards Data Science .
Towards Data Science
Sep 3
My Model Worked Perfectly. Then I Tried to Make It Useful.
The article describes how to deploy a trained churn classifier as a FastAPI service so that other software can call it. It focuses on the practical steps needed to transform a model that performs well in isolation into a usable, callable API. The post is aimed at readers who want to make their machine‑learning models accessible in real-world applications.
Towards Data Science
Jun 2
Code Is Cheap. Engineering Judgement Is Now the Scarce Resource
The barriers to building have collapsed. That shifts the bottleneck to ownership, validation, taste, and deciding what should actually exist The post Code Is Cheap.
Towards Data Science
Aug 20
How to Fine-Tune an LLM: An End-to-End Guide
The article "How to Fine-Tune an LLM: An End-to-End Guide" offers a practical, hands‑on walkthrough for fine‑tuning large language models in real‑world scenarios. It covers the entire process from data preparation to deployment, providing readers with actionable steps to adapt LLMs to specific tasks. The guide is aimed at practitioners looking to implement fine‑tuning in a structured, end‑to‑end manner.
Towards Data Science
Sep 21
Google Offered $10M for a Dying Airline's Data. How Can You Value Yours?
How to put a dollar value on your organization's operational data The post Google Offered $10M for a Dying Airline's Data. How Can You Value Yours? appeared first on Towards Data Science.
Simon Willison
Aug 23
Anthropic’s best AI model struggles to attract users as cheaper tools thrive
Anthropic’s top AI model is struggling to attract users even as cheaper alternatives thrive. The company’s July revenue is projected at $65 bn, up from $47 bn in May, and it expects Q3 profitability while boasting 6,000 high‑spending customers. In contrast, OpenAI’s revenue has risen 35 % this quarter, spurred by GPT‑5.6, and a Ramp AI index shows Anthropic’s newer models (e.g., Fable) are less popular than older ones like Opus 4.8.
Towards Data Science
Jul 21
Are Your ML Experiments a Mess? Here’s the Fix
A hands-on guide to tracking experiments, logging models, and reproducing results with ML Flow. The post Are Your ML Experiments a Mess?
Towards Data Science
Oct 23
Your Six AI Models Are Really Two: The Math of Correlated Mistakes
The article explains that when multiple AI models are asked the same question and their answers are aggregated by voting, the resulting opinions are often less independent than the number of models suggests. It presents a formula that estimates the effective number of independent models and validates this with six open models evaluated on 1,500 exam questions.