Machine Learning in Production

Developing and Optimizing Data Science Workflows and Applications

Andrew Kelleher, Adam Kelleher (Autoren)

Buch | Softcover

288 Seiten

2019
Addison Wesley (Verlag)
978-0-13-411654-9 (ISBN)

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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.

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.

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

Erscheinungsdatum	17.10.2018
Reihe/Serie	Addison-Wesley Data & Analytics Series
Verlagsort	Boston
Sprache	englisch
Maße	180 x 230 mm
Gewicht	561 g
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Informatik ► Web / Internet
ISBN-10	0-13-411654-2 / 0134116542
ISBN-13	978-0-13-411654-9 / 9780134116549
Zustand	Neuware