Managing Machine Learning Projects

Simon Thompson

ISBN 10: 163343902X / ISBN 13: 9781633439023

Verlag: Manning Publications, US, 2023

Beschreibung

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!

Bestandsnummer des Verkäufers LU-9781633439023

Bibliografische Details

• Titel: Managing Machine Learning Projects
• Verlag: Manning Publications, US
• Erscheinungsjahr: 2023
• Sprache: Englisch
• ISBN-10: 163343902X
• ISBN-13: 9781633439023
• Einband: Hardback
• Zustand: New

Über diesen Titel

Inhaltsangabe

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 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 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!

Über die Autorin bzw. den Autor

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 for 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.