Subject: On practical machine learning, taking you from the 101 to being aware of practical edge cases before using in business settings. The parts of MLE you wouldn’t get from coding tutorials or math.
Recomend:
Chip Huyen’s Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications. Very well structured and complete. The GitHub repo also has links to practical production blogs.
Over
Machine Learning Engineering by Andriy Burkov. Not quite as full or well written as Huyen’s, but did cover slightly different topics (eg always need cohort plots; use Kaplan-Meir to estimate cohorts; more talk of agreement metrics; how to calibrate models and how calibrated models are easier to monitor)
Machine Learning Design Patterns by Michael Munn, Sara Robinson, and Valliappa Lakshmanan. Was basically only an introduction to Big Query syntax and GCP offerings. Though the section on data split was a little better than the other 2.
Subject: On practical machine learning, taking you from the 101 to being aware of practical edge cases before using in business settings. The parts of MLE you wouldn’t get from coding tutorials or math.
Recomend:
Chip Huyen’s Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications. Very well structured and complete. The GitHub repo also has links to practical production blogs.
Over
Machine Learning Engineering by Andriy Burkov. Not quite as full or well written as Huyen’s, but did cover slightly different topics (eg always need cohort plots; use Kaplan-Meir to estimate cohorts; more talk of agreement metrics; how to calibrate models and how calibrated models are easier to monitor)
Machine Learning Design Patterns by Michael Munn, Sara Robinson, and Valliappa Lakshmanan. Was basically only an introduction to Big Query syntax and GCP offerings. Though the section on data split was a little better than the other 2.