Hardware Accelerators For Machine Learning A Complete Guide - 2020 Edition. Gerardus Blokdyk

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      45. Does the team have regular meetings?

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      46. What is in scope?

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      47. How does the Hardware accelerators for machine learning manager ensure against scope creep?

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      48. Where can you gather more information?

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      49. What sort of initial information to gather?

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      50. What key stakeholder process output measure(s) does Hardware accelerators for machine learning leverage and how?

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      51. Is the team adequately staffed with the desired cross-functionality? If not, what additional resources are available to the team?

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      52. Are there any constraints known that bear on the ability to perform Hardware accelerators for machine learning work? How is the team addressing them?

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      53. How was the ‘as is’ process map developed, reviewed, verified and validated?

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      54. Who is gathering information?

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      55. Is there regularly 100% attendance at the team meetings? If not, have appointed substitutes attended to preserve cross-functionality and full representation?

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      56. When is the estimated completion date?

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      57. What is the context?

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      58. Is special Hardware accelerators for machine learning user knowledge required?

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      59. How do you gather Hardware accelerators for machine learning requirements?

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      60. How would you define Hardware accelerators for machine learning leadership?

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      61. How do you gather requirements?

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      62. Is the current ‘as is’ process being followed? If not, what are the discrepancies?

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      63. How do you hand over Hardware accelerators for machine learning context?

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      64. How will the Hardware accelerators for machine learning team and the group measure complete success of Hardware accelerators for machine learning?

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      65. Is the Hardware accelerators for machine learning scope complete and appropriately sized?

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      66. Are resources adequate for the scope?

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      67. Has the Hardware accelerators for machine learning work been fairly and/or equitably divided and delegated among team members who are qualified and capable to perform the work? Has everyone contributed?

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      68. Have all basic functions of Hardware accelerators for machine learning been defined?

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      69. Is the work to date meeting requirements?

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      70. What Hardware accelerators for machine learning requirements should be gathered?

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      71. Has everyone on the team, including the team leaders, been properly trained?

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      72. What gets examined?

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      73. What defines best in class?

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      74. If substitutes have been appointed, have they been briefed on the Hardware accelerators for machine learning goals and received regular communications as to the progress to date?

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      75. How and when will the baselines be defined?

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      76. What sources do you use to gather information for a Hardware accelerators for machine learning study?

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      77. Do the problem and goal statements meet the SMART criteria (specific, measurable, attainable, relevant, and time-bound)?

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      78. Has a project plan, Gantt chart, or similar been developed/completed?

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      79. How would you define the culture at your organization, how susceptible is it to Hardware accelerators for machine learning changes?

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      80. What critical content must be communicated – who, what, when, where, and how?

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      81. Who is gathering Hardware accelerators for machine learning information?

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      82. How is the team tracking and documenting its work?

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      83. How do you keep key subject matter experts in the loop?

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      84. Are roles and responsibilities formally defined?

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      85. Have the customer needs been translated into specific, measurable requirements? How?

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      86. When is/was the Hardware accelerators for machine learning start date?

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