The Squawk

Listen to the Squawking Chicken

“An affectionate tribute to her tough, powerful Chinese mother.”—Kirkus Reviews “I devoured this book in one sitting...alternately cheering, laughing, cringing, and gasping in horror. Lui captures the complexity of a mother-daughter relationship that is both complicated and beautiful. Poignant with a bare honesty that may make you think (and rethink) your own relationships.” —Jenny Lawson, #1 New York Times–bestselling author of Let’s Pretend This Never Happened Meet Elaine Lui’s mother. She’s “a movie, an Amy Tan novel, and a sitcom all rolled into one.”* Or as her daughter sums it up: “She’s Chinese, she squawks like a chicken, she is totally nuts, and I am totally dependent on her.” With tales of brutal mah-jong competitions, all-cap texts (“YOUR BAD SKIN NEED SOUP”); public shaming, and pearls of occasionally-bizarre wisdom; Lui not only paints a portrait of a challenging, frustrating, fascinating woman that will make you laugh and cry—she eloquently describes exactly what it’s like to love someone who drives you crazy. “A remarkable memoir about Lui’s relationship with her Hong Kong-born mom, who makes Tiger Mothers look like pussycats.”—Tampa Bay Times *Lisa Gabriele, author and TV producer

Machine Learning Systems

Summary Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology If you’re building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users. About the Book Machine Learning Systems: Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well. What's Inside Working with Spark, MLlib, and Akka Reactive design patterns Monitoring and maintaining a large-scale system Futures, actors, and supervision About the Reader Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed. About the Author Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https: //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. Table of Contents PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING Learning reactive machine learning Using reactive tools PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM Collecting data Generating features Learning models Evaluating models Publishing models Responding PART 3 - OPERATING A MACHINE LEARNING SYSTEM Delivering Evolving intelligence

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