Machine Learning in Production is a crash course in data science and machine learning for learners who
need to solve real-world problems in production environments. Written for technically competent
"accidental data scientists" with more curiosity and ambition than formal training, this complete and
rigorous introduction stresses practice, not theory.
Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver signi¬cant value in
production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive
experience, they help you ask useful questions and then execute production projects from start to -nish.
The authors show just how much information you can glean with straightforward queries, aggregations,
and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They
turn to workhorse machine learning techniques such as linear regression, classi¬cation, clustering, and
Bayesian inference, helping you choose the right algorithm for each production problem. Their
concluding section on hardware, infrastructure, and distributed systems o ers unique and invaluable
guidance on optimization in production environments.
They always focus on what matters in production: solving the problems that o er the highest return on
investment, using the simplest, lowest-risk approaches that work.