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

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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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      102. Is Hardware accelerators for machine learning currently on schedule according to the plan?

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      103. Has a Hardware accelerators for machine learning requirement not been met?

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      104. What is out of scope?

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      105. Has anyone else (internal or external to the group) attempted to solve this problem or a similar one before? If so, what knowledge can be leveraged from these previous efforts?

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      106. What are the tasks and definitions?

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      107. Scope of sensitive information?

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      108. Are task requirements clearly defined?

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      109. Is the team equipped with available and reliable resources?

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      110. What intelligence can you gather?

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      111. What are the core elements of the Hardware accelerators for machine learning business case?

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      112. What information do you gather?

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      113. Are customer(s) identified and segmented according to their different needs and requirements?

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      114. Is there a Hardware accelerators for machine learning management charter, including stakeholder case, problem and goal statements, scope, milestones, roles and responsibilities, communication plan?

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      115. Has/have the customer(s) been identified?

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      116. What are the compelling stakeholder reasons for embarking on Hardware accelerators for machine learning?

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      117. What are the requirements for audit information?

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      118. Are different versions of process maps needed to account for the different types of inputs?

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      119. How do you gather the stories?

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      120. Is there any additional Hardware accelerators for machine learning definition of success?

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      121. The political context: who holds power?

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      122. What are the boundaries of the scope? What is in bounds and what is not? What is the start point? What is the stop point?

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      123. What is the definition of success?

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      124. What information should you gather?

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      125. How did the Hardware accelerators for machine learning manager receive input to the development of a Hardware accelerators for machine learning improvement plan and the estimated completion dates/times of each activity?

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      126. Who defines (or who defined) the rules and roles?

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      127. What scope to assess?

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      128. How do you manage changes in Hardware accelerators for machine learning requirements?

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