Why AI/data science projects fail : how to avoid project pitfalls / Joyce Weiner.Material type: TextSeries: Synthesis lectures on computation and analytics ; #1. | Synthesis digital library of engineering and computer sciencePublisher: San Rafael, California (1537 Fourth Street, 1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, Description: 1 PDF (xi, 65 pages) : illustrationsContent type: text Media type: electronic Carrier type: online resourceISBN: 9781636390390Other title: Why artificial intelligence/data science projects fail : how to avoid project pitfallsSubject(s): Artificial Intelligence | Big data | Project management | data science | project management | AI projects | data science projects | project planning | agile applied to data science | Lean Six SigmaGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 006.3 LOC classification: Q335 | .W458 2021ebOnline resources: Abstract with links to resource | Abstract with links to full text Also available in print.
|Item type||Current library||Call number||Status||Date due||Barcode|
|Ebooks||Indian Institute of Technology Delhi - Central Library||Available|
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Part of: Synthesis digital library of engineering and computer science.
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.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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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.
Also available in print.
Title from PDF title page (viewed on January 15, 2021).