Your Data
It has been collected consistently enough that records from two years ago mean the same
thing as records from last month.
The data needed to train a model exists and is accessible to your team.
You have enough historical examples to represent the range of outcomes you care about,
including the rare or edge-case ones.
You know where the gaps are and have a plan for addressing them.
Your Problem
You can describe the decision you're trying to improve in a single specific sentence.
You know what a good outcome looks like,and how you would measure it.
The problem occurs frequently enough that solving it will have a meaningful impact.
You've ruled out simpler solutions — a well-designed spreadsheet or a workflow
change — that might solve the problem without ML.
Your Organization
Someone owns this project and has the authority to make decisions about it.
The teams who will use the tool have been involved, not just informed.
You have identified who will validate the model's outputs before they influence 
real decisions.
Leadership understands that this is a phased process, not a one-time delivery.
Your Path Forward
You know where the model's output needs to land to actually be used.
You have defined what success looks like at 30, 60, and 90 days.
You have a partner or internal resource with the technical expertise to build and deploy it.
There is a defined process for a human to review, override, and log disagreements with the
model's output.
Machine Learning 
Readiness Checklist
Not every box needs to be checked before you start. But every
unchecked box is worth a conversation before you go too far. 
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