000 04925nam a2200769 i 4500
001 9316369
003 IEEE
005 20220822104650.0
006 m eo d
007 cr bn |||m|||a
008 210115s2021 caua fob 000 0 eng d
020 _a9781636390390
_qelectronic
020 _z9781636390406
_qhardcover
020 _z9781636390383
_qpaperback
024 7 _a10.2200/S01070ED1V01Y202012CAN001
_2doi
035 _a(CaBNVSL)thg00082233
035 _a(OCoLC)1231736040
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQ335
_b.W458 2021eb
082 0 4 _a006.3
_223
100 1 _aWeiner, Joyce,
_eauthor.
245 1 0 _aWhy AI/data science projects fail :
_bhow to avoid project pitfalls /
_cJoyce Weiner.
246 3 _aWhy artificial intelligence/data science projects fail :
_bhow to avoid project pitfalls.
264 1 _aSan Rafael, California (1537 Fourth Street, 1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool Publishers,
_c[2021]
300 _a1 PDF (xi, 65 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on computation and analytics ;
_v#1
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
504 _aIncludes bibliographical references (pages 63-64).
505 0 _a1 Introduction and background -- 2. Project phases and common project pitfalls -- 2.1. Tips for managers
505 8 _a3. 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
505 8 _a4. Define phase -- 4.1. Project charter -- 4.2. Supplier-input-process-output-customer (SIPOC) analysis -- 4.3. Tips for managers
505 8 _a5. 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
505 8 _a6. 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
505 8 _a7. Model-building phase -- 7.1. Keep it simple -- 7.2. Repeatability -- 7.3. Leverage explainability -- 7.4. Tips for managers
505 8 _a8. Interpret and communicate phase -- 8.1. Know your audience -- 8.2. Reports -- 8.3. Presentations -- 8.4. Models -- 8.5. Tips for mangers
505 8 _a9. Deployment phase -- 9.1. Plan for deployment from the start -- 9.2. Documentation -- 9.3. Maintenance -- 9.4. Tips for managers
505 8 _a10. 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.
506 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 _aRecent 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.
530 _aAlso available in print.
588 0 _aTitle from PDF title page (viewed on January 15, 2021).
650 0 _aArtificial Intelligence.
650 0 _aBig data.
650 0 _aProject management.
653 _adata science
653 _aproject management
653 _aAI projects
653 _adata science projects
653 _aproject planning
653 _aagile applied to data science
653 _aLean Six Sigma
655 0 _aElectronic books.
776 0 8 _iPrint version:
_z9781636390383
_z9781636390406
830 0 _aSynthesis lectures on computation and analytics ;
_v#1.
830 0 _aSynthesis digital library of engineering and computer science.
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=9316369
856 4 0 _3Abstract with links to full text
_uhttps://doi.org/10.2200/S01070ED1V01Y202012CAN001
999 _c237351
_d237351