Thread by @quaesita

Leaders! Stop dropping the ball on #machinelearning projects!

This thread is a manual for the leader's role in ML/AI projects, with short guides to each bit. 1/🧵

If you think it's okay for the leader to skip these tasks or punt them to the #AI project's chief nerd, read this: lnkd.in/eziSYhX
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lnkd.in/eziSYhX

First, check that ML/AI is right for you. Don't just chase the buzzword. Use it to solve a real problem! Here's how:
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Do you have a good application? If not, here's how to find one. Video: lnkd.in/drjYwvU9
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lnkd.in/drjYwvU9

Do you have access to the necessary resources? Checklist: lnkd.in/eUxmjCN
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lnkd.in/eUxmjCN

If you're struggling to find a good use case, consider investing in #Analytics before or instead of #MachineLearning. Video: lnkd.in/dcXzMusz and mini-course: lnkd.in/dDUkQcc
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lnkd.in/dDUkQcc

One you've verified that you need #MachineLearning, define your objectives, metrics, and criteria! Here's how:
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Write down possible system outputs and consider mistakes. Video: lnkd.in/d7becMjK
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Assign project scoring. Video: lnkd.in/dAQzhw8M
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Think about aggregate system performance. Video: lnkd.in/du6eAV_b
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Create your business performance metric. Video:
lnkd.in/dwcYurCn
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Create your business performance metric. Video: lnkd.in/dSjEd_mG
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Compare your metric with the loss function. Video: lnkd.in/dE3uZuF9
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Set performance criteria before diving into data. Video: lnkd.in/dn4zzpCY
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