Machine learning in production : developing and optimizing data science workflows and applications / Adam Kelleher, Andrew Kelleher.

Includes index and bibliographical references

I. Principles of framing. The role of the data scientist Project workflow Quantifying error Data encoding and preprocessing Hypothesis testing Data visualization II. Algorithms and architectures. Introduction to algorithms and architectures Comparison Regression Classification and clustering Bayesian networks Dimensional reduction and latent variable models Causal inference Advanced machine learning III. Bottlenecks and optimizations. Hardware fundamentals Software fundamentals Software architecture The CAP theorem Logical network topological nodes

"Machine Learning in Production is a crash course in data science and machine learning for people 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 significant 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 finish...