Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

I. THÔNG TIN SẢN PHẨM

Mã sản phẩm    : STT389
Nhà xuất bản: O'Reilly Media; 1st edition (November 10, 2020)
Số trang    : 408 trang
Tác giả        : Valliappa Lakshmanan, Sara Robinson, Michael Munn
Ngôn Ngữ    : Tiếng Anh
ISBN ‏         : 1098115783
Hình thức    : Bìa Mềm, IN ĐEN TRẮNG
Loại         : Sách gia công
Giấy in        : Giấy ngoại định lượng 70msg, viết vẽ và hightlight thoải mái.
Chất lượng    : Bản in rõ nét, giá rất tốt cho mọi người.

II. MÔ TẢ SẢN PHẨM

1) Mô tả sản phẩm
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.

In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.

You'll learn how to:
    Identify and mitigate common challenges when training, evaluating, and deploying ML models
    Represent data for different ML model types, including embeddings, feature crosses, and more
    Choose the right model type for specific problems
    Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning
    Deploy scalable ML systems that you can retrain and update to reflect new data
    Interpret model predictions for stakeholders and ensure models are treating users fairly

2) Tác giả
Valliappa (Lak) Lakshmanan is Global Head for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA.

Sara Robinson is a Developer Advocate on Google's Cloud Platform team, focusing on machine learning. She inspires developers and data scientists to integrate ML into their applications through demos, online content, and events. Sara has a bachelor’s degree from Brandeis University. Before Google, she was a Developer Advocate on the Firebase team.

Michael Munn is an ML Solutions Engineer at Google where he works with customers of Google Cloud on helping them design, implement, and deploy machine learning models. He also teaches an ML Immersion Program at the Advanced Solutions Lab. Michael has a PhD in mathematics from the City University of New York. Before joining Google, he worked as a research professor.