For anyone interested in better management of machine learning projects from idea to production. Managing Machine Learning Projects is a comprehensive guide that does not require any technical skills. This edition will help you discover battle-tested data infrastructure techniques and will guide you through bringing a project to a successful conclusion.

Guide machine learning projects with the techniques in this unique project management guide.

Managing Machine Learning Projects is a comprehensive guide to delivering successful machine learning projects from idea to production. The book is laid out as a series of fictionalised sprints that take you from pre-project requirements and proposal development all the way to deployment. You will discover battle-tested techniques for ensuring you have the appropriate data infrastructure, coordinating ML experiments, and measuring model performance. With this book as your guide, you will know how to bring a project to a successful conclusion, and how to use your lessons learned for future projects.

About the reader

This book is for anyone interested in better management of machine learning projects. No technical skills are required!

The go-to guide in machine learning projects from design to production. No ML skills required!

In Managing Machine Learning Projects, you will 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 behaviours for managing the ethical implications of ML technology

About the technology

Companies of all shapes, sizes, and industries are investing in machine learning (ML). Unfortunately, around 85% of all ML projects fail. Managing machine learning projects requires adopting a different approach than you would take with standard software projects.

You need to account for large and diverse data resources, evaluate and track multiple separate models, and handle the unforeseeable risk of poor performance. Never fear — this book lays out the unique practices you will need to ensure your projects succeed!