Record:   Prev Next
Author Weiner, Joyce, author
Title Why AI/data science projects fail : how to avoid project pitfalls / Joyce Weiner
Imprint [San Rafael, California] : Morgan & Claypool Publishers, [2021]
book jacket
Descript 1 online resource (xi, 65 pages) : illustrations
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Synthesis lectures on computation and analytics ; #1
Synthesis lectures on computation and analytics ; 1
Note Includes bibliographical references (pages 63-64)
1. Introduction and background -- 2. Project phases and common project pitfalls -- 2.1. Tips for managers -- 3. Five methods to avoid common pitfalls -- 3.1. Ask questions -- 3.2. Get alignment -- 3.3. Keep it simple -- 3.4. Leverage explainability -- 3.5. Have the conversation -- 3.6. Tips for managers -- 4. Define phase -- 4.1. Project charter -- 4.2. Supplier-input-process-output-customer (SIPOC) analysis -- 4.3. Tips for managers -- 5. Making the business case : assigning value to your project -- 5.1. Data analysis projects -- 5.2. Automation projects -- 5.3. Improving business processes -- 5.4. Data mining projects -- 5.5. Improved data science -- 5.6. Metrics to dollar conversion -- 6. Acquisition and exploration of data phase -- 6.1. Acquiring data -- 6.2. Developing data collection systems -- 6.3. Data exploration -- 6.4. What does the customer want to know? -- 6.5. Preparing for a report or model -- 6.6. Tips for managers -- 7. Model-building phase -- 7.1. Keep it simple -- 7.2. Repeatability -- 7.3. Leverage explainability -- 7.4. Tips for managers -- 8. Interpret and communicate phase -- 8.1. Know your audience -- 8.2. Reports -- 8.3. Presentations -- 8.4. Models -- 8.5. Tips for mangers -- 9. Deployment phase -- 9.1. Plan for deployment from the start -- 9.2. Documentation -- 9.3. Maintenance -- 9.4. Tips for managers -- 10. Summary of the five methods to avoid common pitfalls -- 10.1. Ask questions -- 10.2. Get alignment -- 10.3. Keep it simple -- 10.4. Leverage explainability -- 10.5. Have the conversation
Recent data shows that 87% of Artificial Intelligence/Big Data projects don't make it into production (VB Staff, 2019), meaning that most projects are never deployed. This book addresses five common pitfalls that prevent projects from reaching deployment and provides tools and methods to avoid those pitfalls. Along the way, stories from actual experience in building and deploying data science projects are shared to illustrate the methods and tools. While the book is primarily for data science practitioners, information for managers of data science practitioners is included in the Tips for Managers sections
Online resource; title from digital title page (viewed on January 21, 2021)
Link Print version: Weiner, Joyce. Why AI/data science projects fail. [San Rafael, California] : Morgan & Claypool Publishers, 2021 9781636390383 9781636390406 (OCoLC)1232949549
Subject Artificial intelligence
Big data
Project management
Artificial intelligence. fast (OCoLC)fst00817247
Big data. fast (OCoLC)fst01892965
Project management. fast (OCoLC)fst01078797
data science
project management
AI projects
data science projects
project planning
agile applied to data science
Lean Six Sigma
Electronic books
Alt Title Why artificial intelligence/data science projects fail : how to avoid project pitfalls
Record:   Prev Next