Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
- Paperback: 288 pages
- Publisher: WOW! eBook; 1st edition (March 14, 2019)
- Language: English
- ISBN-10: 0134116542
- ISBN-13: 978-0134116549
Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications: Foundational Hands-On Skills for Succeeding with Real Data Science Projects
This book 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.
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, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments.