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 you took at weddings, without any manual tagging needed.
- Flagging spam and offensive text content among the posts of a budding chat app.
- Building a music recommendation system for users of an online radio.
- Detecting credit card fraud for an e-commerce website.
- Predicting display ad click-through-rate to make the decision of which ad to serve to a given user at a given time.
- Flagging anomalous cookies on the conveyor belt of a cookie-manufacturing line.
- Using satellite images to predict the location of as-of-yet unknown archeological sites.
It would be very convenient if you could import the correct dataset from keras.datasets and start fitting some deep learning models. Unfortunately, in the real world, you’ll have to start from scratch.