From the course: Data Science on Google Cloud Platform: Predictive Analytics
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Creating a training package - Google Cloud Tutorial
From the course: Data Science on Google Cloud Platform: Predictive Analytics
Creating a training package
- [Presenter] So far, we have been building models locally. This type of task typically uses a small subset of data. With this data, we try different algorithms and settings before arriving at an optimal one. We can then run this optimal algorithm on a large set of data. We can do so with Cloud ML. Cloud ML will use its scaling capabilities to execute this step faster and on a large data set. In order to do that, we need to create a training package. Let's use Cloud Shell for this. We can also do this on any Compute Engine instance. We first create a data tree called propensity_trainer with the following command: mkdir propensity_trainer. Then, we navigate to this directly. We create an empty file called __init__.py using the following command: touch__init__.py. Next, we copy our propensity-cloud.py file into this folder. This is the package we have to create for Cloud ML to work on this code.
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