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.
CRITERION #2: DEFINE:
INTENT: Formulate the stakeholder problem. Define the problem, needs and objectives.
In my belief, the answer to this question is clearly defined:
5 Strongly Agree
4 Agree
3 Neutral
2 Disagree
1 Strongly Disagree
1. Are all requirements met?
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2.
