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The AI playbook : mastering the rare art of machine learning deployment / Eric Siegel.

By: Material type: TextTextLanguage: eng Series: Management on the cutting edgePublisher: Cambridge, Massachusetts : The MIT Press, [2024]Description: 225 pages : illustration ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780262048903
Subject(s): Additional physical formats: Online version:: AI playbookDDC classification:
  • 658/.05 23/eng/20230908
LOC classification:
  • HF5548.2 S571a 2024
Contents:
ContentsSeries Foreword ix Foreword by Morgan Vawter xi Preface: A Brief History of Why Machine Learning Projects Stall xv Optional FAQ: What This Book Is about and Who It’s For xxiIntroduction 1 0 BizML: Six Steps to Machine Learning Deployment 21 1 Value: Establish the Deployment Goal 49 2 Target: Establish the Prediction Goal 63 3 Performance: Establish the Evaluation Metrics 81 4 Fuel: Prepare the Data 113 5 Algorithm: Train the Model 141 6 Launch: Deploy the Model 169 BizML Cheat Sheet 195 Conclusion: ML’s Elevator Pitch, Staff, Timeline, Upkeep, and Ethics 197Acknowledgments 213 About the Author 217 Index 219
Summary: "A playbook for bridging business and data science worlds to effectively execute machine learning projects in business"-- Provided by publisher.
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Holdings
Item type Current library Home library Collection Shelving location Call number Copy number Status Date due Barcode
Libro Libro Biblioteca Juan Bosch Biblioteca Juan Bosch Automatización y Procesos Técnicos Automatización y Procesos Técnicos (1er. Piso) HF5548.2 S571a 2024 (Browse shelf(Opens below)) 1 Available 00000190374

Includes bibliographical references and index.

ContentsSeries Foreword ix Foreword by Morgan Vawter xi Preface: A Brief History of Why Machine Learning Projects Stall xv Optional FAQ: What This Book Is about and Who It’s For xxiIntroduction 1 0 BizML: Six Steps to Machine Learning Deployment 21 1 Value: Establish the Deployment Goal 49 2 Target: Establish the Prediction Goal 63 3 Performance: Establish the Evaluation Metrics 81 4 Fuel: Prepare the Data 113 5 Algorithm: Train the Model 141 6 Launch: Deploy the Model 169 BizML Cheat Sheet 195 Conclusion: ML’s Elevator Pitch, Staff, Timeline, Upkeep, and Ethics 197Acknowledgments 213 About the Author 217 Index 219

"A playbook for bridging business and data science worlds to effectively execute machine learning projects in business"-- Provided by publisher.

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