See how to modify Python code to execute on GCP and use cloud sources in this video.
- Let's take a look at how we can modify…the model building code for propensity to work with DCP.…The code is available in the file,…propensity-cloud.py.…This code should be run from the Cloud Shell…or from a Compute Engine command line.…Let's look at the key changes…required to enable cloud here.…First, in line 15, we need to read the…file from cloud storage.…
The part for the file in cloud storage is…available in this variable data file,…then we use an OS command in line number 17…to call the gsutil command and do a copy of…this file from cloud into a local file.…So the file is being copied from…Google Cloud Storage to a local data tree.…Then the model building process…continues as original.…Finally, once the model is built,…we need to save that generated model into cloud storage.…
For that, we go to line number 57.…The model file has to be created with the name…model.joblib.…Then we dump the model that we created into this variable.…We then upload this file into cloud storage,…we create a data tree in cloud storage…
- 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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