Serverless Machine Learning with Amazon Redshift ML by Debu Panda & Phil Bates & Bhanu Pittampally & Sumeet Joshi

Author:Debu Panda & Phil Bates & Bhanu Pittampally & Sumeet Joshi
Language: eng
Format: epub
Publisher: Packt
Published: 2023-11-15T00:00:00+00:00

Figure 6.19 – The customer count by segment

Now that you have this information, your marketing team is ready to target their efforts on these prospective customers.

Let’s now take a look at some other options you can use to solve this multi-class classification problem.

Exploring other CREATE MODEL options

We can also create this model in a couple of different ways, which we will explore in the following sections. It is important to understand the different options available so that you can experiment and choose the approach that gives you the best model.

In the first example, we will not provide any user guidance, such as specifying MODEL_TYPE, PROBLEM_TYPE, or OBJECTIVE. Use this approach if you are new to ML and want to let SageMaker Autopilot determine this for you.

Then, in the next example, you can see how you can provide PROBLEM_TYPE and OBJECTIVE. As a more experienced user of ML, you should now recognize which PROBLEM_TYPE and OBJECTIVE instances are best for your use case. When you provide these inputs, it will speed up the model training process, since SageMaker Autopilot will only train using the provided user guidance.

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