Amazon machine learning モデルの概念図
what is amazon machine learning
amazon ML can be used to make predictions for a variety of purposes. For example, you could build a model in Amazon ML that will predict whether a given customer is likely to respond to a marketing offer. Amazon ML creates models from supervised data sets. This means that the model is based on a set of previous observations. This set of observations consists of features or attributes as well as the target outcome. In the marketing offer example, the features might include the age, profession, and gender of the customer. The target outcome (also called the target variable) would be whether that particular customer responded to the marketing offer or not.
The process of creating a model from a set of known observations is called training. Once you have trained a model in Amazon ML, you can then use the model to predict outcomes from a set of attributes that matches the attributes used to train the model. Amazon ML scales so that you can make thousands of predictions concurrently. This is important, as today machine learning is often used to provide predictions in near real-time. In this lab, you will be using a machine learning model to predict which restaurants a customer is likely to favor based on the results of a search query.
data setをs3のバケットのuploadする。
なるほど、S3はこういう風に使うのね。
machine learningを選択する
流石にまだ翻訳されてないな。
