Learn how to create a training package for deploying on Cloud ML.
- [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.…
- Evaluating the machine learning tools in GCP
- Understanding the predictive analytics process
- Building models
- Training models with jobs
- Building and running predictions
- Best practices for cost control, testing, and performance monitoring
Skill Level Intermediate
Predictive Customer Analyticswith Kumaran Ponnambalam1h 37m Intermediate
1. ML Options in GCP
2. Cloud ML Basics
3. Model Building with Cloud ML
4. Predictions in Cloud ML
5. Cloud ML Best Practices
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