SIMON THOMPSON

Green and Sustainable Finance offers a comprehensive guide to green and sustainable principles and practice in banking investment and insurance

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 youll learn essential machine learning project management techniques, including: Understanding an ML projects 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. Youll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The books 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 youll need to ensure your projects succeed. About the BookManaging 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. Youll learn how to plan essential infrastructure, coordinate experimentation, protect sensitive data, and reliably measure model performance. Many ML projects fail to create real valueread this book to make sure your project is a success.Whats 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 AuthorSimon Thompson has spent 25 years developing AI systems to create applications for use in telecoms, customer service, manufacturing and capital markets. He led the AI research program at BT Labs in the UK, and is now the Head of Data Science at GFT Technologies. Table of Contents 1 Introduction: Delivering machine learning projects is hard; lets 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 ...