<p>Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.</p>

<p>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. <b><i>Agile Machine Learning </i></b>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.</p>

<p>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.</p><p><br></p><p><b> What You'll Learn</b></p>

<p></p><ul><li>Effectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focused<br></li><li>Make sound implementation and model exploration decisions based on the data and the metrics<br></li><li>Know the importance of data wallowing: analyzing data in real time in a group setting<br></li><li>Recognize the value of always being able to measure your current state objectively<br></li><li>Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations<br></li></ul><p></p>











<p><b><br></b></p><p><b>Who This Book Is For</b></p><p>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.</p>