6 The universal workflow of machine learning
This chapter covers
- Steps for framing a machine learning problem
- Steps for developing a working model
- Steps for deploying your model in production and maintaining it
Our previous examples have assumed that we already had a labeled dataset to start from, and that we could immediately start training a model. In the real world, this is often not the case. You don’t start from a dataset, you start from a problem.
Imagine that you’re starting your own machine learning consulting shop. You incorporate, you put up a fancy website, you notify your network. The projects start rolling in:
-
A personalized photo search engine for a picture-sharing social network—type in “wedding” and retrieve all the pictures ...
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