Author: Eric Carter, Matthew Hurst Full Title: Agile Machine Learning: Effective Machine Learning Inspired By The Agile Manifesto Publisher: Apress; 1st ed. edition (August 22, 2019) Year: 2019 ISBN-13: 9781484251072 (978-1-4842-5107-2), 9781484251065 (978-1-4842-5106-5) ISBN-10: 1484251075, 1484251067 Pages: 248 Language: English Genre: Educational: Microsoft and .NET File type: EPUB (True), PDF (True) Quality: 10/10 Price: 40.65 € Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment. The authors’ approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. Learn: ✓ Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused ✓ Make sound implementation and model exploration decisions based on the data and the metrics ✓ Know the importance of data wallowing: analyzing data in real time in a group setting ✓ Recognize the value of always being able to measure your current state objectively ✓ Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations Features: ✓ Authors have proven real-world experience with numerous big data projects coordinated across distributed teams for multiple Microsoft markets ✓ Teaches you how to manage projects involving machine learning more effectively in a production environment ✓ Shows you, by example, how to deliver superior data products through agile processes and organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment Who This Book Is For: Anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data. ------------- Our members see more. Join us!