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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

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  • Your Price: $39.99
  • List Price: $49.99
  • Estimated Release: Jan 21, 2019

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  • Your Price: $31.99
  • List Price: $39.99
  • Estimated Release: Jan 21, 2019
  • Includes EPUB, MOBI, and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

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About

Features

  • Practical principles and step-by-step techniques for transforming any business “ask” into actionable insight
  • Includes two complete end-to-end site optimization projects, with reusable code and open source tools recommendations readers can easily adapt for their own projects
  • Ideal for all junior and aspiring data scientists -- and for all software developers, engineers, and others with new responsibilities related to data science
  • By Andrew and Adam Kelleher, twin brothers playing pivotal roles in data science and engineering at BuzzFeed
  • No heavy math or statistics background required!

Description

  • Copyright 2019
  • Dimensions: 7" x 9-1/8"
  • Pages: 288
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-411654-2
  • ISBN-13: 978-0-13-411654-9

Foundational Hands-On Skills for Succeeding with Real Data Science Projects

This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings.

–From the Foreword by Paul Dix, series editor

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 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.

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 maximize development efficiency in production projects
  • Learn from practical Python code examples and visualizations 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 visualization 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

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Sample Content

Table of Contents

Preface

Acknowledgments

About the Authors

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 Pre-Processing

Chapter 5: Hypothesis Testing

Chapter 6: Data Visualization

Part II: Algorithms and Architectures

Chapter 7: 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

Index

Updates

Submit Errata

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