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.…
Author
Released
11/7/2018- 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
Duration
Views
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Introduction
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1. ML Options in GCP
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Cloud Natural Language1m 20s
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Cloud Translation1m 17s
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2. Cloud ML Basics
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Models56s
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Model versions41s
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Jobs56s
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Predictive analytics process1m 55s
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3. Model Building with Cloud ML
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Understanding input data1m 30s
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Build and test model locally1m 53s
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Modify code to work with GCP1m 42s
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Creating a training package1m 21s
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Training using jobs3m 49s
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4. Predictions in Cloud ML
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Creating a model version2m 10s
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Running a prediction1m 37s
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5. Cloud ML Best Practices
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Cost control1m 17s
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Local testing1m 12s
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Performance monitoring1m 35s
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Conclusion
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Next steps41s
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Video: Creating a training package