Читать книгу Hardware Accelerators For Machine Learning A Complete Guide - 2020 Edition - Gerardus Blokdyk - Страница 7

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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47. What Hardware accelerators for machine learning capabilities do you need?

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48. What Hardware accelerators for machine learning coordination do you need?

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49. How can auditing be a preventative security measure?

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50. What else needs to be measured?

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51. Is the quality assurance team identified?

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52. What is the Hardware accelerators for machine learning problem definition? What do you need to resolve?

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53. What training and capacity building actions are needed to implement proposed reforms?

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54. Does Hardware accelerators for machine learning create potential expectations in other areas that need to be recognized and considered?

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55. What problems are you facing and how do you consider Hardware accelerators for machine learning will circumvent those obstacles?

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56. How do you assess your Hardware accelerators for machine learning workforce capability and capacity needs, including skills, competencies, and staffing levels?

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57. Who needs to know about Hardware accelerators for machine learning?

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58. Will it solve real problems?

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59. Would you recognize a threat from the inside?

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60. What are the minority interests and what amount of minority interests can be recognized?

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61. Is it needed?

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62. What are the Hardware accelerators for machine learning resources needed?

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63. Do you know what you need to know about Hardware accelerators for machine learning?

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64. When a Hardware accelerators for machine learning manager recognizes a problem, what options are available?

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65. Is the need for organizational change recognized?

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66. For your Hardware accelerators for machine learning project, identify and describe the business environment, is there more than one layer to the business environment?

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67. What is the recognized need?

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68. Are there regulatory / compliance issues?

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69. What is the problem or issue?

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70. What vendors make products that address the Hardware accelerators for machine learning needs?

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71. What would happen if Hardware accelerators for machine learning weren’t done?

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72. What extra resources will you need?

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73. Will new equipment/products be required to facilitate Hardware accelerators for machine learning delivery, for example is new software needed?

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74. What are the clients issues and concerns?

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75. What Hardware accelerators for machine learning events should you attend?

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76. Are employees recognized or rewarded for performance that demonstrates the highest levels of integrity?

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77. Who needs budgets?

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78. Are your goals realistic? Do you need to redefine your problem? Perhaps the problem has changed or maybe you have reached your goal and need to set a new one?

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79. Think about the people you identified for your Hardware accelerators for machine learning project and the project responsibilities you would assign to them, what kind of training do you think they would need to perform these responsibilities effectively?

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80. Do you need to avoid or amend any Hardware accelerators for machine learning activities?

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81. Are there any revenue recognition issues?

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82. Looking at each person individually – does every one have the qualities which are needed to work in this group?

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83. Who needs to know?

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84. How much are sponsors, customers, partners, stakeholders involved in Hardware accelerators for machine learning? In other words, what are the risks, if Hardware accelerators for machine learning does not deliver successfully?

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85. Consider your own Hardware accelerators for machine learning project, what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?

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86. What should be considered when identifying available resources, constraints, and deadlines?

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87. What are the timeframes required to resolve each of the issues/problems?

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88. What do you need to start doing?

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89. What tools and technologies are needed for a custom Hardware accelerators for machine learning project?

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90. Will Hardware accelerators for machine learning deliverables need to be tested and, if so, by whom?

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91. Do you need different information or graphics?

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92. What is the extent or complexity of the Hardware accelerators for machine learning problem?

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93. How does it fit into your organizational needs and tasks?

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94. What needs to stay?

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95. Are you dealing with any of the same issues today as yesterday? What can you do about this?

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96. What needs to be done?

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97. What do employees need in the short term?

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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.