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

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      CRITERION #2: DEFINE:

      INTENT: Formulate the stakeholder problem. Define the problem, needs and objectives.

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

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      1. Are all requirements met?

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      2. What are the Hardware accelerators for machine learning tasks and definitions?

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      3. What is the scope?

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      4. Is data collected and displayed to better understand customer(s) critical needs and requirements.

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      5. Is it clearly defined in and to your organization what you do?

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      6. How do you build the right business case?

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      7. What is a worst-case scenario for losses?

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      8. Do you all define Hardware accelerators for machine learning in the same way?

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      9. Has the improvement team collected the ‘voice of the customer’ (obtained feedback – qualitative and quantitative)?

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      10. Who approved the Hardware accelerators for machine learning scope?

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      11. Have all of the relationships been defined properly?

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      12. How are consistent Hardware accelerators for machine learning definitions important?

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      13. What Hardware accelerators for machine learning services do you require?

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      14. Do you have organizational privacy requirements?

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      15. When are meeting minutes sent out? Who is on the distribution list?

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      16. Are approval levels defined for contracts and supplements to contracts?

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      17. How do you manage unclear Hardware accelerators for machine learning requirements?

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      18. What are the Roles and Responsibilities for each team member and its leadership? Where is this documented?

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      19. What baselines are required to be defined and managed?

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      20. What is the definition of Hardware accelerators for machine learning excellence?

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      21. Who are the Hardware accelerators for machine learning improvement team members, including Management Leads and Coaches?

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      22. What is out-of-scope initially?

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      23. Is Hardware accelerators for machine learning linked to key stakeholder goals and objectives?

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      24. What happens if Hardware accelerators for machine learning’s scope changes?

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      25. What system do you use for gathering Hardware accelerators for machine learning information?

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      26. What is the worst case scenario?

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      27. How do you catch Hardware accelerators for machine learning definition inconsistencies?

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      28. Has your scope been defined?

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      29. Have specific policy objectives been defined?

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      30. What are the Hardware accelerators for machine learning use cases?

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      31. Are audit criteria, scope, frequency and methods defined?

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      32. Do you have a Hardware accelerators for machine learning success story or case study ready to tell and share?

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      33. Is Hardware accelerators for machine learning required?

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      34. Has a high-level ‘as is’ process map been completed, verified and validated?

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      35. What is the scope of the Hardware accelerators for machine learning work?

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      36. In what way can you redefine the criteria of choice clients have in your category in your favor?

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      37. How will variation in the actual durations of each activity be dealt with to ensure that the expected Hardware accelerators for machine learning results are met?

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      38. How can the value of Hardware accelerators for machine learning be defined?

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      39. Has a team charter been developed and communicated?

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      40. What are (control) requirements for Hardware accelerators for machine learning Information?

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      41. Why are you doing Hardware accelerators for machine learning and what is the scope?

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      42. How do you manage scope?

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      43. What customer feedback methods were used to solicit their input?

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      44. How do you think the partners involved in Hardware accelerators for machine learning would have defined success?

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      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