Status: Hardware Accelerators For Machine Learning253
4.4 Risk Audit: Hardware Accelerators For Machine Learning255
4.5 Contractor Status Report: Hardware Accelerators For Machine Learning257
4.6 Formal Acceptance: Hardware Accelerators For Machine Learning259
5.0 Closing Process Group: Hardware Accelerators For Machine Learning261
5.1 Procurement Audit: Hardware Accelerators For Machine Learning263
5.2 Contract Close-Out: Hardware Accelerators For Machine Learning265
5.3 Project or Phase Close-Out: Hardware Accelerators For Machine Learning267
5.4 Lessons Learned: Hardware Accelerators For Machine Learning269
Index271
CRITERION #1: RECOGNIZE
INTENT: Be aware of the need for change. Recognize that there is an unfavorable variation, problem or symptom.
In my belief, the answer to this question is clearly defined:
5 Strongly Agree
4 Agree
3 Neutral
2 Disagree
1 Strongly Disagree
1. Who else hopes to benefit from it?
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2. Who defines the rules in relation to any given issue?
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3. How are the Hardware accelerators for machine learning’s objectives aligned to the group’s overall stakeholder strategy?
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4. As a sponsor, customer or management, how important is it to meet goals, objectives?
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5. What situation(s) led to this Hardware accelerators for machine learning Self Assessment?
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6. Who needs what information?
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7. Can management personnel recognize the monetary benefit of Hardware accelerators for machine learning?
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8. Are there recognized Hardware accelerators for machine learning problems?
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9. Which needs are not included or involved?
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10. Which issues are too important to ignore?
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11. Are losses recognized in a timely manner?
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12. Are there Hardware accelerators for machine learning problems defined?
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13. How many trainings, in total, are needed?
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14. Will a response program recognize when a crisis occurs and provide some level of response?
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15. What are your needs in relation to Hardware accelerators for machine learning skills, labor, equipment, and markets?
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16. To what extent would your organization benefit from being recognized as a award recipient?
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17. What does Hardware accelerators for machine learning success mean to the stakeholders?
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18. Where do you need to exercise leadership?
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19. How are you going to measure success?
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20. Whom do you really need or want to serve?
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21. What are the stakeholder objectives to be achieved with Hardware accelerators for machine learning?
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22. What Hardware accelerators for machine learning problem should be solved?
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23. What is the smallest subset of the problem you can usefully solve?
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24. What are the expected benefits of Hardware accelerators for machine learning to the stakeholder?
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25. How do you identify the kinds of information that you will need?
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26. Who should resolve the Hardware accelerators for machine learning issues?
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27. To what extent does each concerned units management team recognize Hardware accelerators for machine learning as an effective investment?
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28. Why the need?
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29. Are there any specific expectations or concerns about the Hardware accelerators for machine learning team, Hardware accelerators for machine learning itself?
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30. Do you recognize Hardware accelerators for machine learning achievements?
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31. What is the problem and/or vulnerability?
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32. How are training requirements identified?
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33. Why is this needed?
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34. Which information does the Hardware accelerators for machine learning business case need to include?
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35. How do you take a forward-looking perspective in identifying Hardware accelerators for machine learning research related to market response and models?
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36. Have you identified your Hardware accelerators for machine learning key performance indicators?
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37. Did you miss any major Hardware accelerators for machine learning issues?
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38. Who are your key stakeholders who need to sign off?
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39. Are problem definition and motivation clearly presented?
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40. What information do users need?
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41. How do you identify subcontractor relationships?
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42. Where is training needed?
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43. What activities does the governance board need to consider?
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44. Are employees recognized for desired behaviors?
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45. Does the problem have ethical dimensions?
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46. How do you recognize an Hardware accelerators for machine learning objection?
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