Managing Machine Learning Projects

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 requirementsSetting up the infrastructure for the project and resourcing a teamWorking with clients and other stakeholdersDealing with data resources and bringing them into the project for useHandling the lifecycle of models in the projectManaging the application of ML algorithmsEvaluating the performance of algorithms and modelsMaking decisions about which models to adopt for deliveryTaking models through development and testingIntegrating models with production systems to create effective applicationsSteps 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!