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 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.

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