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

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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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      129. What are the dynamics of the communication plan?

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      130. What constraints exist that might impact the team?

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      131. Is scope creep really all bad news?

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      132. What specifically is the problem? Where does it occur? When does it occur? What is its extent?

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      133. What is in the scope and what is not in scope?

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      134. Is there a completed SIPOC representation, describing the Suppliers, Inputs, Process, Outputs, and Customers?

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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 #3: MEASURE:

      INTENT: Gather the correct data. Measure the current performance and evolution of the situation.

      In my belief, the answer to this question is clearly defined:

      5 Strongly Agree

      4 Agree

      3 Neutral

      2 Disagree

      1 Strongly Disagree

      1. What is the cost of rework?

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      2. Are the units of measure consistent?

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      3. What users will be impacted?

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      4. Was a business case (cost/benefit) developed?

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      5. What is the Hardware accelerators for machine learning business impact?

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      6. What could cause you to change course?

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      7. How will success or failure be measured?

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      8. How can you reduce costs?

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      9. Do the benefits outweigh the costs?

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      10. Is the solution cost-effective?

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      11. What would it cost to replace your technology?

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      12. How do you control the overall costs of your work processes?

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      13. How do you verify Hardware accelerators for machine learning completeness and accuracy?

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      14. What are the operational costs after Hardware accelerators for machine learning deployment?

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      15. Why a Hardware accelerators for machine learning focus?

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      16. The approach of traditional Hardware accelerators for machine learning works for detail complexity but is focused on a systematic approach rather than an understanding of the nature of systems themselves, what approach will permit your organization to deal with the kind of unpredictable emergent behaviors that dynamic complexity