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 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

• 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

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.