Managing Machine Learning Projects: From design to deployment

Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills required!

In Managing Machine Learning Projects you’ll learn essential machine learning project management techniques, including:
• Understanding an ML project’s requirements
• Setting up the infrastructure for the project and resourcing a team
• Working with clients and other stakeholders
• Dealing with data resources and bringing them into the project for use
• Handling the lifecycle of models in the project
• Managing the application of ML algorithms
• Evaluating the performance of algorithms and models
• Making decisions about which models to adopt for delivery
• Taking models through development and testing
• Integrating models with production systems to create effective applications
• Steps and behaviors for managing the ethical implications of ML technology

Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. You’ll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The book’s strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues.

About the Technology
Ferrying machine learning projects to production often feels like navigating uncharted waters. From accounting for large data resources to tracking and evaluating multiple models, machine learning technology has radically different requirements than traditional software. Never fear! This book lays out the unique practices you’ll need to ensure your projects succeed.

About the Book
Managing Machine Learning Projects is an amazing source of battle-tested techniques for effective delivery of real-life machine learning solutions. The book is laid out across a series of sprints that take you from a project proposal all the way to deployment into production. You’ll learn how to plan essential infrastructure, coordinate experimentation, protect sensitive data, and reliably measure model performance. Many ML projects fail to create real value—read this book to make sure your project is a success.

What's Inside
• Set up infrastructure and resource a team
• Bring data resources into a project
• Accurately estimate time and effort
• Evaluate which models to adopt for delivery
• Integrate models into effective applications