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Katherine Munro

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Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics

Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics

Data Science and AI: what they are, how they're applied in diverse industries, and how to learn them.

2022

Annalisa Cadonna, et al.

Annalisa Cadonna, Bernhard Ortner, et al.

Stories

In

Towards AI

by

Katherine Munro

·

Sep 3, 2025

LLMs as Judges: Practical Problems and How to Avoid Them

Concrete advice for teams building LLM-powered evaluations

A collection of sticky notes and emojis related to evaluation (graphs, ticks and crosses, etc)

In

Towards AI

by

Katherine Munro

·

Jul 9, 2025

Large Language Models as Classification Engines: Overkill, or Awesome?

Why is it an odd choice? Why you should try it anyway. And how to go about it.

A simple cartoon featuring speech bubbles paired up with various labels.

In

Data Science Collective

by

Katherine Munro

·

Apr 27, 2025

Using one LLM to Judge Another? Here’s Five Reasons You Shouldn’t

Concrete ‘gotchas’ for people building things with Gen AI.

A collection of sticky notes with ‘watch out!’, ‘gotcha’, and other such messages, which will be discussed in this article.

In

Data Science Collective

by

Katherine Munro

·

Feb 11, 2025

From Benchmarks to the Big Time: Evaluating LLM Systems with Production Data at Scale

Presenting a DIY framework for fixing the gaps in your testing strategy when building LLM-powered products

From Benchmarks to the Big Time: Evaluating LLM Systems with Production Data at Scale

In

TDS Archive

by

Katherine Munro

·

Aug 26, 2024

No Baseline? No Benchmarks? No Biggie! An Experimental Approach to Agile Chatbot Development

Lessons learned bringing LLM-based products to production

A slide from my talk, summarizing the current state and goal, as described in this article.

In

TDS Archive

by

Katherine Munro

·

Jul 5, 2024

How Big Tech Is Exploiting Content Creators, And (Trying To) Get Away With It

Diving into current courtroom dramas around AI, copyright, and the true meaning of “fair use.”

Union of Musicians and Allied Workers protesting at Spotify’s corporate headquarters in San Francisco.

In

TDS Archive

by

Katherine Munro

·

May 3, 2024

From Probabilistic to Predictive: Methods for Mastering Customer Lifetime Value

The final chapter in a comprehensive, practical guide to real-world applications of CLV analysis & prediction

A graph with Customer’s recency on the y axis (values from 35 to 0 as you travel up the axis), and Customer’s Frequency on the x axis (values from 0 to 25). The entire graph is filled with a colour gradient reflecting the probability that a customer is alive. Close to both axes, the colours indicate high P-Alive. But most of the graph, spreading from the upper right corner, indicates low P-Alive.

In

TDS Archive

by

Katherine Munro

·

Feb 12, 2024

Yes, you still need old-school NLP skills in “the age of ChatGPT”

Large Language Models are amazing, but for many production problems, simpler techniques are faster, cheaper, and just as…

An old fashioned typewriter with a piece of paper that simply reads “Update”.

In

TDS Archive

by

Katherine Munro

·

Nov 17, 2023

Methods for Modelling Customer Lifetime Value: The Good Stuff and the Gotchas

Part three of a comprehensive, practical guide to CLV techniques and real-world use-cases

A hand-drawn chart showing the size of different customer segments as a proportion of the overall customer base.

In

TDS Archive

by

Katherine Munro

·

Aug 7, 2023

Congrats on your Customer Lifetime Value prediction model — now what are you going to do with it?

An obsessively detailed guide to Customer Lifetime Value techniques and real-world applications

Congrats on your Customer Lifetime Value prediction model — now what are you going to do with it?

Katherine Munro

Katherine Munro

Book Author

Data Scientist, speaker, author, teacher. Follow me on Medium or Twitter (@KatherineAMunro) for resources on data science, AI, tech, ethics, and more.

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