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
Managing Machine Learning Projects is an end-to-end guide for project managers who need to deliver machine learning applications on time and under budget. It gives you a unique set of tools, approaches, and processes designed to handle the unique requirements of machine learning project management—all proven in practice to deliver success in full-scale business environments. You’ll follow an in-depth case study of a Bike Shop developing their first machine learning application and see how to put each technique into practice. Throughout, the book gives strong consideration to the ethical issues of ML, including data privacy, and community impact. You’ll learn how to avoid and mitigate these issues and keep your machine learning project from being exposed to failure.
About the Technology
Companies of all shapes, sizes, and industries are investing in machine learning (ML). Unfortunately, something like 85% of all ML projects fail. Managing machine learning projects requires adopting a different approach than you’d take with standard software projects. You’ll need to account for large and diverse data resources, evaluating and tracking multiple separate models, and handling the unforeseeable risk of poor performance. 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 a comprehensive guide to delivering successful machine learning projects from idea to production. The book is laid out as a series of fictionalized sprints that take you from pre-project requirements and proposal development all the way to deployment. You’ll 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’ll know how to bring a project to a successful conclusion, and how to use your lessons learned for future projects.
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
about the reader
For anyone interested in better management of machine learning projects. No technical skills required.
About the Author
Simon Thompson has spent 25 years developing AI systems. He led the AI research program at BT Labs in the UK, where he helped pioneer Big Data technology in the company and managed an applied research practice for nearly a decade. Simon now works delivering Machine Learning systems for financial services companies in the City of London as the Head of Data Science at GFT Technologies.
Table of Contents
1. Introduction: Delivering Machine Learning projects is hard, let’s do it better
2. Pre-project: From opportunity to requirements
3. Pre-project: From requirements to proposal
4. Getting started
5. Diving into the problem
6. EDA, ethics, and baseline evaluations
7. Making useful models with ML
8. Testing and selection
9. System building and production
10. Post project (Sprint Ω)
