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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data & Analytics Series) - Softcover
Synopsis
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.
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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.
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- PublisherAddison-Wesley Professional
- Publication date2019
- ISBN 10 0134116542
- ISBN 13 9780134116549
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages288
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data & Analytics Series)
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Addison-Wesley Professional (edition 1), 2019
ISBN 10: 0134116542
ISBN 13: 9780134116549
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data Analytics Series)
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Addison-Wesley Professional, 2019
ISBN 10: 0134116542
ISBN 13: 9780134116549
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Machine Learning in Production : Developing and Optimizing Data Science Workflows and Applications
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data & Analytics Series)
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ISBN 13: 9780134116549
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data & Analytics Series)
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ISBN 10: 0134116542
ISBN 13: 9780134116549
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data & Analytics Series)
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ISBN 13: 9780134116549
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Machine Learning in Production (Paperback)
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Paperback. Condition: new. Paperback. 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. The authors show just how much information you can glean with straightforward queries, aggregations, and visualisations, 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 optimisation in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximise development efficiency in production projects Learn from practical Python code examples and visualisations that bring essential algorithmic concepts to life Start with simple heuristics and improve them as your data pipeline matures Avoid bad conclusions by implementing foundational error analysis techniques Communicate your results with basic data visualisation techniques Master basic machine learning techniques, starting with linear regression and random forests Perform classification and clustering on both vector and graph data Learn the basics of graphical models and Bayesian inference Understand correlation and causation in machine learning models Explore overfitting, model capacity, and other advanced machine learning techniques Make informed architectural decisions about storage, data transfer, computation, and communication Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780134116549
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data & Analytics Series)
Published by
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ISBN 10: 0134116542
ISBN 13: 9780134116549
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
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