Kirk Borne’s book picks
Kirk Borne recommended two new books — Machine Learning Platform Engineering and Machine Learning System Design — as practical primers for building scalable ML systems and platforms. The endorsements push platform engineering and ML system thinking as core skills for modern SWE roles. (x.com)
Machine Learning Platform Engineering is authored by Benjamin Tan Wei Hao, Shanoop Padmanabhan, and Varun Mallya, published by Manning in February 2026 and listed at 504 pages. (manning.com) The Manning listing names core infrastructure and MLOps tools taught in the book: Docker, Kubernetes, MLflow, Kubeflow, BentoML, Evidently, Feast and LangChain. (manning.com) The book guides readers through three full builds—a containerized ML platform with experiment tracking and feature store, two applied ML apps (an ID-card object detector and a movie recommender), and an LLM Retrieval-Augmented Generation assistant called DakkaBot. (livebook.manning.com) Machine Learning System Design is authored by Valerii Babushkin and Arseny Kravchenko, published by Manning in January 2025 with a 376-page handbook-style format. (manning.com) That System Design title lays out a step-by-step framework with requirement checklists, metrics-and-evaluation guidance, dataset problem-solving patterns, and explicit “ML system design interview tips” in its contents. (manning.com) Kirk Borne remains an active ML influencer on X with roughly 380,700 followers and a history of promoting practical ML engineering resources on his timeline, according to archived profile analytics. (thevisualized.com)
