45. Does the team have regular meetings?
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46. What is in scope?
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47. How does the Hardware accelerators for machine learning manager ensure against scope creep?
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48. Where can you gather more information?
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49. What sort of initial information to gather?
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50. What key stakeholder process output measure(s) does Hardware accelerators for machine learning 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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