| 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 |
|
