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Chapter 14Using Cloud AutoML ‒ Tables solutionLet's see an example of using Cloud AutoML Tables (see Figure 4). We'll aim toimport some tabular data and train a classifier on that data; we'll use some marketingdata from a bank. Note that this and the following examples might be chargedby Google according to different usage criteria (please check online for latest costestimations, at https://cloud.google.com/products/calculator/):Figure 4: Google Cloud AutoMLAs of the end of 2019, AutoML Tables is still in beta. Thus, we need to enable the betaAPI (see Figure 5):Figure 5: AutoML Tables beta API[ 499 ]

An introduction to AutoMLThen, we can create a new dataset (see Figure 6 and 7) and import the data (seeFigure 8):Figure 6: AutoML Tables: the initial interfaceFigure 7: AutoML Tables: create a new datasetFor our example we use a demo dataset stored in Google Cloud storage inside thebucket gs:://cloud-ml-tables-data/bank-marketing.csv:[ 500 ]

An introduction to AutoML

Then, we can create a new dataset (see Figure 6 and 7) and import the data (see

Figure 8):

Figure 6: AutoML Tables: the initial interface

Figure 7: AutoML Tables: create a new dataset

For our example we use a demo dataset stored in Google Cloud storage inside the

bucket gs:://cloud-ml-tables-data/bank-marketing.csv:

[ 500 ]

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