Strategy, discovery, metrics, growth, pricing, design, delivery and leadership. Each entry says when it is the right one to pick up.
Huyen's book on building applications on top of foundation models rather than training them. Covers evaluation methodology in depth, prompt engineering, retrieval augmented generation and context construction, finetuning and when …
Written for the people who work with data scientists rather than for the people who will build the models. It teaches the fundamental concepts, including how a business problem maps onto a data mining task, why overfitting happens …
Huyen treats machine learning as a production systems problem, covering everything around the model rather than the modelling itself. The book moves from framing a business problem as a machine learning problem and deciding …
Agrawal, Gans and Goldfarb frame machine learning in economic terms as a drop in the cost of prediction. When prediction gets cheap, its complements of judgement, data and action become more valuable.
Burkov compresses the field into a book you can read in a weekend, with the mathematics present but held to what is genuinely needed. It covers supervised learning from linear and logistic regression through decision trees …
O'Neil, a mathematician who worked in finance and advertising technology, examines algorithms that operate at scale, remain opaque to the people they judge, and cause damage that feeds back into their own training data.