Notes on getting machine learning into production and keeping it there.
We are preparing notes on the parts of applied machine learning that get least attention: framing a problem so a prediction changes a decision, the data work nobody budgets for, and building an assurance argument in a regulated setting.
Suggest a topicWe will say plainly whether machine learning is the right tool for it.
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