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