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

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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      87. What scope do you want your strategy to cover?

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      88. Is the Hardware accelerators for machine learning scope manageable?

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      89. What are the record-keeping requirements of Hardware accelerators for machine learning activities?

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      90. Is the improvement team aware of the different versions of a process: what they think it is vs. what it actually is vs. what it should be vs. what it could be?

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      91. What knowledge or experience is required?

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      92. What would be the goal or target for a Hardware accelerators for machine learning’s improvement team?

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      93. What is the scope of the Hardware accelerators for machine learning effort?

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      94. Has the direction changed at all during the course of Hardware accelerators for machine learning? If so, when did it change and why?

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      95. What are the rough order estimates on cost savings/opportunities that Hardware accelerators for machine learning brings?

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      96. Will a Hardware accelerators for machine learning production readiness review be required?

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      97. What was the context?

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      98. Is there a completed, verified, and validated high-level ‘as is’ (not ‘should be’ or ‘could be’) stakeholder process map?

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      99. Is there a critical path to deliver Hardware accelerators for machine learning results?

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      100. Are there different segments of customers?

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      101. How often are the team meetings?

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