Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications / Edition 1

by Andrew Kelleher, Adam Kelleher

Overview

The typical data science task in industry starts with an “ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Written by two of the experts who’ve achieved breakthrough optimizations at BuzzFeed, it’s packed with real-world examples that take you from start to finish: from ask to actionable insight.

Andrew Kelleher and Adam Kelleher walk you through well-formed, concrete principles for approaching common data science problems, giving you an easy-to-use checklist for effective execution. Using their principles and techniques, you’ll gain deeper understanding of your data, learn how to analyze noise and confounding variables so they don’t compromise your analysis, and save weeks of iterative improvement by planning your projects more effectively upfront.

Once you’ve mastered their principles, you’ll put them to work in two realistic, beginning-to-end site optimization tasks. These extended examples come complete with reusable code examples and recommended open-source solutions designed for easy adaptation to your everyday challenges. They will be especially valuable for anyone seeking their first data science job — and everyone who’s found that job and wants to succeed in it.

Product Details

ISBN-13:	9780134116549
Publisher:	Pearson Education
Publication date:	05/08/2019
Series:	Addison-Wesley Data & Analytics Series
Pages:	288
Product dimensions:	7.00(w) x 9.00(h) x 0.70(d)

About the Author

Andrew Kelleher is a staff software engineer and distributed systems architect at Venmo. He was previously a staff software engineer at BuzzFeed and has worked on data pipelines and algorithm implementations for modern optimization. He graduated with a BS in physics from Clemson University. He runs a meetup in New York City that studies the fundamentals behind distributed systems in the context of production applications, and was ranked one of FastCompany's most creative people two years in a row.

Adam Kelleher wrote this book while working as principal data scientist at BuzzFeed and adjunct professor at Columbia University in the City of New York. As of May 2018, he is chief data scientist for research at Barclays and teaches causal inference and machine learning products at Columbia. He graduated from Clemson University with a BS in physics, and has a PhD in cosmology from University of North Carolina at Chapel Hill.

Table of Contents

• Part I: Principles of Framing
• Chapter 1: The Role of the Data Scientist
• Chapter 2: Project Workflow
• Chapter 3: Quantifying Error
• Chapter 4: Data Encoding and Preprocessing
• Chapter 5: Hypothesis Testing
• Chapter 6: Data Visualization
• Part II: Algorithms and Architectures
• Chapter 7: Introduction to Algorithms and Architectures
• Chapter 8: Comparison
• Chapter 9: Regression
• Chapter 10: Classification and Clustering
• Chapter 11: Bayesian Networks
• Chapter 12: Dimensional Reduction and Latent Variable Models
• Chapter 13: Causal Inference
• Chapter 14: Advanced Machine Learning
• Part III: Bottlenecks and Optimizations
• Chapter 15: Hardware Fundamentals
• Chapter 16: Software Fundamentals
• Chapter 17: Software Architecture
• Chapter 18: The CAP Theorem
• Chapter 19: Logical Network Topological Nodes
• Bibliography