Читать книгу Hardware Accelerators For Machine Learning A Complete Guide - 2020 Edition - Gerardus Blokdyk - Страница 9
ОглавлениеCRITERION #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?
<--- Score
2. Are the units of measure consistent?
<--- Score
3. What users will be impacted?
<--- Score
4. Was a business case (cost/benefit) developed?
<--- Score
5. What is the Hardware accelerators for machine learning business impact?
<--- Score
6. What could cause you to change course?
<--- Score
7. How will success or failure be measured?
<--- Score
8. How can you reduce costs?
<--- Score
9. Do the benefits outweigh the costs?
<--- Score
10. Is the solution cost-effective?
<--- Score
11. What would it cost to replace your technology?
<--- Score
12. How do you control the overall costs of your work processes?
<--- Score
13. How do you verify Hardware accelerators for machine learning completeness and accuracy?
<--- Score
14. What are the operational costs after Hardware accelerators for machine learning deployment?
<--- Score
15. Why a Hardware accelerators for machine learning focus?
<--- Score
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 can introduce?
<--- Score
17. Are there any easy-to-implement alternatives to Hardware accelerators for machine learning? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
<--- Score
18. Have you included everything in your Hardware accelerators for machine learning cost models?
<--- Score
19. How can you reduce the costs of obtaining inputs?
<--- Score
20. What causes mismanagement?
<--- Score
21. What are allowable costs?
<--- Score
22. Did you tackle the cause or the symptom?
<--- Score
23. How are measurements made?
<--- Score
24. Where is it measured?
<--- Score
25. Why do you expend time and effort to implement measurement, for whom?
<--- Score
26. Are there measurements based on task performance?
<--- Score
27. What can be used to verify compliance?
<--- Score
28. What are the costs?
<--- Score
29. What are the current costs of the Hardware accelerators for machine learning process?
<--- Score
30. What methods are feasible and acceptable to estimate the impact of reforms?
<--- Score
31. What are the types and number of measures to use?
<--- Score
32. What are the Hardware accelerators for machine learning investment costs?
<--- Score
33. What is the total cost related to deploying Hardware accelerators for machine learning, including any consulting or professional services?
<--- Score
34. How do you measure variability?
<--- Score
35. What is the cause of any Hardware accelerators for machine learning gaps?
<--- Score
36. What is an unallowable cost?
<--- Score
37. How will you measure success?
<--- Score
38. How do your measurements capture actionable Hardware accelerators for machine learning information for use in exceeding your customers expectations and securing your customers engagement?
<--- Score
39. At what cost?
<--- Score
40. What do people want to verify?
<--- Score
41. Does management have the right priorities among projects?
<--- Score
42. What does losing customers cost your organization?
<--- Score
43. What does a Test Case verify?
<--- Score
44. Are you aware of what could cause a problem?
<--- Score
45. What happens if cost savings do not materialize?
<--- Score
46. What does verifying compliance entail?
<--- Score
47. Are indirect costs charged to the Hardware accelerators for machine learning program?
<--- Score
48. Among the Hardware accelerators for machine learning product and service cost to be estimated, which is considered hardest to estimate?
<--- Score
49. How sensitive must the Hardware accelerators for machine learning strategy be to cost?
<--- Score
50. What is measured? Why?
<--- Score
51. How will your organization measure success?
<--- Score
52. How can a Hardware accelerators for machine learning test verify your ideas or assumptions?
<--- Score
53. What tests verify requirements?
<--- Score
54. How do you verify the authenticity of the data and information used?
<--- Score
55. How do you verify performance?
<--- Score
56. How do you verify the Hardware accelerators for machine learning requirements quality?
<--- Score
57. Which measures and indicators matter?
<--- Score
58. When are costs are incurred?
<--- Score
59. Are you taking your company in the direction of better and revenue or cheaper and cost?
<--- Score
60. What do you measure and why?
<--- Score
61. What measurements are being captured?
<--- Score
62. What are the uncertainties surrounding estimates of impact?
<--- Score
63. How do you measure efficient delivery of Hardware accelerators for machine learning services?
<--- Score
64. Are there competing Hardware accelerators for machine learning priorities?
<--- Score
65. What measurements are possible, practicable and meaningful?
<--- Score
66. Do you have an issue in getting priority?
<--- Score
67. What are your customers expectations and measures?
<--- Score
68. Is it possible to estimate the impact of unanticipated complexity such as wrong or failed assumptions, feedback, etcetera on proposed reforms?
<--- Score
69. Has a cost center been established?
<--- Score
70. How is progress measured?
<--- Score
71. Are the Hardware accelerators for machine learning benefits worth its costs?
<--- Score
72. What harm might be caused?
<--- Score
73. Where can you go to verify the info?
<--- Score
74. How to cause the change?
<--- Score
75. What is your Hardware accelerators for machine learning quality cost segregation study?
<--- Score
76. How frequently do you verify your Hardware accelerators for machine learning strategy?
<--- Score
77. How will effects be measured?
<--- Score
78. What is the root cause(s) of the problem?
<--- Score
79. Are the measurements objective?
<--- Score
80. Does the Hardware accelerators for machine learning task fit the client’s priorities?
<--- Score
81. Will Hardware accelerators for machine learning have an impact on current business continuity, disaster recovery processes and/or infrastructure?
<--- Score
82. How frequently do you track Hardware accelerators for machine learning measures?
<--- Score
83. Are you able to realize any cost savings?
<--- Score
84. What are your key Hardware accelerators for machine learning organizational performance measures, including key short and longer-term financial measures?
<--- Score
85. What details are required of the Hardware accelerators for machine learning cost structure?
<--- Score
86. What would be a real cause for concern?
<--- Score
87. What are the strategic priorities for this year?
<--- Score
88. Does a Hardware accelerators for machine learning quantification method exist?
<--- Score
89. Do you verify that corrective actions were taken?
<--- Score
90. Is the cost worth the Hardware accelerators for machine learning effort ?
<--- Score
91. Do you have a flow diagram of what happens?
<--- Score
92. Which Hardware accelerators for machine learning impacts are significant?
<--- Score
93. Do you have any cost Hardware accelerators for machine learning limitation requirements?
<--- Score
94. What are hidden Hardware accelerators for machine learning quality costs?
<--- Score
95. Is there an opportunity to verify requirements?
<--- Score
96. How will you measure your Hardware accelerators for machine learning effectiveness?
<--- Score
97. What does your operating model cost?
<--- Score
98. What evidence is there and what is measured?
<--- Score
99. How do you aggregate measures across priorities?
<--- Score
100. What is the total fixed cost?
<--- Score
101. What potential environmental factors impact the Hardware accelerators for machine learning effort?
<--- Score
102. Do you aggressively reward and promote the people who have the biggest impact on creating excellent Hardware accelerators for machine learning services/products?
<--- Score
103. Which costs should be taken into account?
<--- Score
104. Who should receive measurement reports?
<--- Score
105. Are supply costs steady or fluctuating?
<--- Score
106. Who is involved in verifying compliance?
<--- Score
107. How can you measure Hardware accelerators for machine learning in a systematic way?
<--- Score
108. What are the costs of reform?
<--- Score
109. What are your primary costs, revenues, assets?
<--- Score
110. Where is the cost?
<--- Score
111. How much does it cost?
<--- Score
112. How do you measure lifecycle phases?
<--- Score
113. What disadvantage does this cause for the user?
<--- Score
114. Have design-to-cost goals been established?
<--- Score
115. Do you effectively measure and reward individual and team performance?
<--- Score
116. How do you prevent mis-estimating cost?
<--- Score
117. When should you bother with diagrams?
<--- Score
118. How long to keep data and how to manage retention costs?
<--- Score
119. How are costs allocated?
<--- Score
120. What are you verifying?
<--- Score
121. How do you verify if Hardware accelerators for machine learning is built right?
<--- Score
122. How will costs be allocated?
<--- Score
123. What are the estimated costs of proposed changes?
<--- Score
124. How do you quantify and qualify impacts?
<--- Score
125. What drives O&M cost?
<--- Score
126. What could cause delays in the schedule?
<--- Score
127. Who pays the cost?
<--- Score
