Score
129. What are the dynamics of the communication plan?
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130. What constraints exist that might impact the team?
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131. Is scope creep really all bad news?
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132. What specifically is the problem? Where does it occur? When does it occur? What is its extent?
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133. What is in the scope and what is not in scope?
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134. Is there a completed SIPOC representation, describing the Suppliers, Inputs, Process, Outputs, and Customers?
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Add up total points for this section: _____ = Total points for this section
Divided by: ______ (number of statements answered) = ______ Average score for this section
Transfer your score to the Hardware accelerators for machine learning Index at the beginning of the Self-Assessment.
CRITERION #3: MEASURE:
INTENT: Gather the correct data. Measure the current performance and evolution of the situation.
In my belief, the answer to this question is clearly defined:
5 Strongly Agree
4 Agree
3 Neutral
2 Disagree
1 Strongly Disagree
1. What is the cost of rework?
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2. Are the units of measure consistent?
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3. What users will be impacted?
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4. Was a business case (cost/benefit) developed?
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5. What is the Hardware accelerators for machine learning business impact?
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6. What could cause you to change course?
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7. How will success or failure be measured?
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8. How can you reduce costs?
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9. Do the benefits outweigh the costs?
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10. Is the solution cost-effective?
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11. What would it cost to replace your technology?
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12. How do you control the overall costs of your work processes?
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13. How do you verify Hardware accelerators for machine learning completeness and accuracy?
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14. What are the operational costs after Hardware accelerators for machine learning deployment?
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15. Why a Hardware accelerators for machine learning focus?
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16. The approach of traditional Hardware accelerators for machine learning works for detail complexity but is focused on a systematic approach rather than an understanding of the nature of systems themselves, what approach will permit your organization to deal with the kind of unpredictable emergent behaviors that dynamic complexity can introduce?
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17. Are there any easy-to-implement alternatives to Hardware accelerators for machine learning? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
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18. Have you included everything in your Hardware accelerators for machine learning cost models?
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19. How can you reduce the costs of obtaining inputs?
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20. What causes mismanagement?
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21. What are allowable costs?
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22. Did you tackle the cause or the symptom?
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23. How are measurements made?
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24. Where is it measured?
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25. Why do you expend time and effort to implement measurement, for whom?
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26. Are there measurements based on task performance?
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27. What can be used to verify compliance?
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28. What are the costs?
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29. What are the current costs of the Hardware accelerators for machine learning process?
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30. What methods are feasible and acceptable to estimate the impact of reforms?
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31. What are the types and number of measures to use?
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32. What are the Hardware accelerators for machine learning investment costs?
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33. What is the total cost related to deploying Hardware accelerators for
